AI Stack · Predictive → Generative → Agentic → Physical · Class 001 Enrolling Now

Physical AI Engineer

A career-focused, hands-on program across the four waves of AI: Predictive AI that forecasts, Generative AI that creates, Agentic AI that acts in software — and Physical AI that perceives, reasons and acts in the real world through robots, sensors and simulation.

10,000+

alumni transformed

1,000+

hiring partners

4.8/5

avg class rating

87%

placed in 6 months

10+

years of training

Where our AI alumni work

Microsoft

Amazon

Salesforce

ServiceNow

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Microsoft

Amazon

Salesforce

ServiceNow

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Direct answer

What is Physical AI — and why learn all four waves?

Physical AI is AI that perceives, reasons and acts in the real world — robots, autonomous machines and smart infrastructure driven by foundation models trained largely in simulation. It sits on top of everything before it: predictive models for perception and forecasting, generative models for language and vision, and agentic loops for planning and tool use. This program builds the stack in that order, so the robot you ship at the end is the sum of what you learned, not a demo bolted on.

The four waves of AI Each wave builds on the last — you learn them in order.

01PREDICTIVE

•Supervised & unsupervised ML
•Time-series forecasting
•Computer vision classifiers
•MLOps: train → deploy → monitor

02GENERATIVE

•LLMs, prompting & RAG
•Fine-tuning & small models
•Vision-language models
•Evaluation & hallucination control

03AGENTIC

•Tool use, MCP & function calling
•Planning & multi-agent systems
•Memory, state & long-horizon tasks
•Guardrails, evals & observability

04PHYSICAL

•ROS 2, sensors & perception
•Simulation & synthetic data
•Vision-language-action (VLA) policies
•Sim-to-real & safety certification

What a Physical AI engineer owns

•Perception pipelines that work on real sensors, not curated datasets
•Policies trained in simulation that transfer to hardware
•Agentic control loops with hard safety limits and human override
•Evaluation evidence — success rate, latency, failure modes — before deployment

And remaining accountable for what the machine does in the world.

Physical AI landscape · 2026

AI left the screen. The jobs followed it.

Robot foundation modelsVLA policies

Vision-language-action models such as NVIDIA GR00T N1, Google Gemini Robotics and Physical Intelligence π0 turn camera frames and language instructions directly into motor actions — one model, many robots and tasks.

Simulation-first trainingIsaac Sim · Cosmos

Policies are trained on synthetic data and digital twins (Omniverse, Isaac Sim, Isaac Lab, Cosmos world models, MuJoCo) and transferred to hardware — sim-to-real is now the core engineering skill.

Humanoids & mobile manipulationHardware wave

Figure, Tesla Optimus, Agility Digit, Unitree and Boston Dynamics Atlas moved from labs to pilot deployments in logistics and manufacturing; software talent is the bottleneck, not hardware.

Edge AI computeJetson Thor · Orin

Onboard inference on NVIDIA Jetson, Qualcomm and custom silicon runs perception, planning and VLA policies in real time — engineers must optimise for latency, power and safety, not just accuracy.

Agentic orchestrationSoftware → world

The same agent loops used for coding and enterprise workflows — planning, tool use, memory, MCP — now dispatch fleets of robots and coordinate them with enterprise systems.

Safety, standards & assuranceISO 10218 · 13482 · EU AI Act

Functional safety, human-robot collaboration standards and AI-risk regulation now decide whether a system can deploy. Evidence, not demos, is what gets a robot signed off.

What this means for your career: the fastest-growing AI roles combine ML, LLM and agent skills with robotics, simulation and edge deployment — engineers who can carry a model from notebook to a machine acting safely in the world are scarce and command a premium.

Who should join

Built for engineers who want AI to act in the real world.

Software engineers moving into AI Data scientists & ML engineers Mechanical, electrical & mechatronics graduates Robotics & embedded engineers GenAI / agent developers adding hardware Automotive, manufacturing & logistics engineers

Prior experience: basic Python and school-level math. Module 1 rebuilds Python, linear algebra and probability from scratch; no robotics hardware experience is assumed — simulation comes first.

What you will be able to do

Take a model from notebook to a machine — safely.

Build predictive modelsTrain, evaluate and deploy ML and computer-vision models with monitoring and drift detection.

Engineer with LLMsPrompting, RAG, fine-tuning and vision-language models with rigorous evaluation.

Design AI agentsTool use, MCP, planning, memory and multi-agent orchestration with guardrails.

Program robots with ROS 2Sensors, perception, SLAM, motion planning and manipulation on real and simulated robots.

Train in simulation, deploy to hardwareIsaac Sim / Lab, synthetic data, RL and imitation learning, domain randomisation and sim-to-real transfer.

Certify safetyFailure-mode analysis, safety envelopes, human override and evidence packs aligned to ISO and the EU AI Act.

Course curriculum

Sixteen modules. Predictive → Generative → Agentic → Physical AI.

Python, Math & Data for AI

Foundations

Rebuild the toolkit every wave depends on: Python for engineering, the linear algebra and probability behind models, and clean data pipelines.

Topics

•Python for engineers — NumPy, pandas, typing, testing

•Linear algebra, calculus and probability for ML

•Data collection, cleaning and versioning

•Git, Docker and reproducible environments

•GPU basics and CUDA-aware workflows

•Experiment tracking with MLflow / W&B;

Hands-on lab

Build a reproducible data pipeline for a sensor dataset (IMU + camera) with versioned splits and a tracked baseline model.

Failure scenario you solve

A team's model 'worked in the notebook' but nobody can rerun it three weeks later — no seeds, no data version, no environment file.

You ship

Reproducible repoVersioned datasetBaseline experiment

Assessed outcome: Reproduce any result in the program from a clean machine in under 30 minutes.

Machine Learning Foundations

Predictive AI

Supervised and unsupervised learning done properly: the models, the evaluation and the failure modes.

Topics

•Regression, classification and tree ensembles

•Feature engineering and leakage

•Cross-validation, metrics and calibration

•Clustering, dimensionality reduction, anomaly detection

•Bias, variance and generalisation

•Explainability — SHAP and error analysis

Hands-on lab

Train and compare gradient-boosted and linear models for equipment-failure prediction; deliver a calibrated model with an error-analysis report.

Failure scenario you solve

A 98%-accurate model is useless in production because the positive class is 1% of the data and nobody looked at recall.

You ship

Model comparison reportCalibrated classifierError analysis

Assessed outcome: Choose, train and defend a model for a business problem using the right metric.

Deep Learning & Computer Vision

Predictive AI

Neural networks from first principles to production vision models — the perception layer robots will later rely on.

Topics

•PyTorch — tensors, autograd, training loops

•CNNs, ResNets and transfer learning

•Object detection (YOLO, DETR) and segmentation

•Vision Transformers and self-supervised features

•Data augmentation and synthetic data

•Model compression — quantisation, pruning, ONNX / TensorRT

Hands-on lab

Fine-tune a detector for warehouse objects, export to ONNX/TensorRT and hit a 30 FPS budget on an edge GPU.

Failure scenario you solve

The detector scores 0.9 mAP on the benchmark but misses every object under the factory's sodium lighting.

You ship

Trained detectorEdge-optimised modelLatency benchmark

Assessed outcome: Ship a vision model that meets an accuracy and latency budget on real footage.

Time Series, Forecasting & MLOps

Predictive AI

Predict what happens next and keep models honest after deployment.

Topics

•Time-series features, seasonality and leakage

•Classical (ARIMA, Prophet) vs deep forecasters

•Predictive maintenance and anomaly detection

•Model serving — FastAPI, batch vs streaming

•Monitoring, drift detection and retraining triggers

•CI/CD for models and feature stores

Hands-on lab

Deploy a demand / failure forecaster behind an API with drift monitoring and an automated retraining pipeline.

Failure scenario you solve

A forecaster trained on 2024 data quietly degrades for six months; nobody set a drift alarm.

You ship

Forecasting serviceMonitoring dashboardRetraining pipeline

Assessed outcome: Operate a predictive model in production with measurable, alerted quality.

LLM Foundations & Prompt Engineering

Generative AI

How transformers actually work, how to steer them, and how to measure whether they are right.

Topics

•Transformer architecture, tokens and attention

•Open vs closed models — GPT, Claude, Gemini, Llama, Qwen

•Prompting patterns, structured outputs and function calling

•Context windows, cost and latency trade-offs

•Evaluation — golden sets, LLM-as-judge, human review

•Hallucination, prompt injection and safety basics

Hands-on lab

Build an evaluated LLM assistant for maintenance manuals with structured outputs and a 200-case regression suite.

Failure scenario you solve

A prompt change that 'looked better' silently broke 30% of previously correct answers — there was no eval set.

You ship

Prompt libraryEval harnessModel selection memo

Assessed outcome: Choose a model and prompt strategy with evidence, and detect regressions automatically.

RAG, Embeddings & Knowledge Systems

Generative AI

Ground models in your own data — documents, telemetry, manuals — reliably.

Topics

•Embeddings, vector stores and hybrid search

•Chunking, metadata and retrieval evaluation

•Advanced RAG — reranking, query rewriting, graph RAG

•Multimodal retrieval — images, diagrams, video frames

•Citations, grounding checks and freshness

•Cost and latency engineering

Hands-on lab

Build a multimodal RAG system over robot maintenance manuals and incident images; measure retrieval precision and answer faithfulness.

Failure scenario you solve

The chatbot cites a procedure from a decommissioned robot model because chunk metadata never included the hardware version.

You ship

RAG serviceRetrieval eval reportGrounded answer UI

Assessed outcome: Deliver a grounded assistant with measured retrieval quality and citations.

Fine-tuning, Small Models & Multimodal GenAI

Generative AI

When prompting is not enough: adapt models, shrink them for the edge, and work across text, image and video.

Topics

•LoRA / QLoRA fine-tuning and instruction data

•Small language models for on-device use

•Distillation and quantisation for edge

•Vision-language models (VLMs) and captioning

•Diffusion models and synthetic image generation

•World models — Cosmos, video generation for simulation

Hands-on lab

Fine-tune a small VLM to describe scenes from a robot camera and run it quantised on a Jetson-class device.

Failure scenario you solve

The cloud VLM is accurate but takes 4 seconds per frame — the robot has 200 ms.

You ship

Fine-tuned VLMQuantised edge modelSynthetic image set

Assessed outcome: Adapt and deploy a multimodal model under real compute constraints.

AI Agents: Tools, MCP & Planning

Agentic AI

From a chat model to a system that decides and acts — with the discipline that makes it safe.

Topics

•Agent loops — perceive, plan, act, reflect

•Tool / function calling and the Model Context Protocol (MCP)

•Frameworks — LangGraph, OpenAI Agents SDK, Google ADK

•Planning, task decomposition and ReAct patterns

•Memory — short-term state, long-term stores

•Error handling, retries and human-in-the-loop

Hands-on lab

Build an operations agent that reads telemetry via MCP tools, plans a maintenance response and requests human approval before acting.

Failure scenario you solve

An agent with unrestricted tools 'helpfully' deletes a work order queue while cleaning up duplicates.

You ship

MCP tool serverAgent with approval gatesTrace logs

Assessed outcome: Design an agent whose every action is traceable, bounded and reversible.

Multi-Agent Systems & Orchestration

Agentic AI

Coordinate specialised agents across long-horizon tasks and enterprise systems.

Topics

•Orchestrator / worker and hierarchical patterns

•Agent-to-agent protocols (A2A) and handoffs

•Long-running workflows, state machines and durability

•Integrating with enterprise systems and APIs

•Cost control, rate limits and caching

•Observability — traces, spans and evaluation in production

Hands-on lab

Build a multi-agent dispatch system that plans warehouse tasks, assigns them to simulated robots and reports status to an ERP.

Failure scenario you solve

Two agents loop forever negotiating who owns a task; the bill is $400 by morning.

You ship

Multi-agent systemDurable workflowObservability dashboard

Assessed outcome: Run a multi-agent system with bounded cost and full traceability.

Agent Evaluation, Guardrails & Governance

Agentic AI

Prove an agent is safe enough to ship — the skill that separates demos from deployments.

Topics

•Task-level evals, success rates and benchmarks

•Red-teaming — prompt injection, tool abuse, jailbreaks

•Guardrails — policy layers, allow-lists, output filters

•Permissions, identity and least privilege for agents

•Audit logs, incident response and rollback

•EU AI Act, NIST AI RMF and organisational governance

Hands-on lab

Red-team the module 8 agent, add guardrails, and produce a governance pack with risk register and evaluation evidence.

Failure scenario you solve

A supplier email containing hidden instructions redirects the procurement agent to approve its own invoice.

You ship

Red-team reportGuardrail layerGovernance pack

Assessed outcome: Deliver evaluation and governance evidence a risk team will sign off.

Robotics Foundations & ROS 2

Physical AI

The operating system of Physical AI: how robots sense, move and are programmed.

Topics

•Robot types — arms, mobile robots, humanoids, drones

•Kinematics, dynamics and coordinate frames

•ROS 2 — nodes, topics, services, actions, launch

•Sensors — cameras, depth, LiDAR, IMU, force/torque

•Simulation basics — Gazebo, MuJoCo, Isaac Sim

•Hardware interfaces, real-time and safety stops

Hands-on lab

Bring up a simulated mobile manipulator in ROS 2, publish sensor streams and command joint and base motion.

Failure scenario you solve

The arm's camera frame is 3 cm off from URDF — every grasp misses and the team blames the model.

You ship

ROS 2 workspaceRobot description (URDF)Sensor pipeline

Assessed outcome: Program a robot's sense-and-move loop in ROS 2 with correct frames and safety stops.

Perception, SLAM & Scene Understanding

Physical AI

Turn raw sensor streams into a world the robot can reason about.

Topics

•Camera calibration and 3D geometry

•Depth, point clouds and sensor fusion

•SLAM and localisation (Nav2, visual SLAM)

•3D object detection, pose estimation and tracking

•Open-vocabulary perception with VLMs and foundation features

•Occupancy, semantic maps and scene graphs

Hands-on lab

Build a perception stack that maps a warehouse, localises the robot and detects and tracks pallets with 6-DoF poses.

Failure scenario you solve

Localisation drifts 2 m on a shiny floor; the robot parks in a doorway.

You ship

SLAM mapPerception nodePose-tracking benchmark

Assessed outcome: Deliver a perception stack with measured accuracy on real sensor logs.

Simulation, Synthetic Data & Digital Twins

Physical AI

Where Physical AI is actually trained: photoreal simulation and world models at scale.

Topics

•NVIDIA Isaac Sim / Omniverse and OpenUSD

•Isaac Lab for RL and imitation learning at scale

•Domain randomisation and synthetic data generation

•World foundation models — Cosmos — for data augmentation

•Digital twins of factories and warehouses

•Sim-to-real gap analysis and closing strategies

Hands-on lab

Build a digital twin of a pick-and-place cell, generate 50k randomised synthetic images and train a detector that transfers to real frames.

Failure scenario you solve

The sim policy is perfect; on hardware the gripper closes 40 ms late and drops every part.

You ship

Digital twin sceneSynthetic datasetSim-to-real report

Assessed outcome: Generate synthetic data and quantify the sim-to-real gap before touching hardware.

Robot Learning: RL, Imitation & VLA Policies

Physical AI

Teach robots skills — from reinforcement learning to vision-language-action foundation models.

Topics

•Reinforcement learning for control (PPO, SAC) in Isaac Lab

•Imitation learning, teleoperation and demonstration data

•Diffusion policies and action chunking

•Vision-language-action models — GR00T N1, π0, OpenVLA, Gemini Robotics

•Fine-tuning VLA models on your own demonstrations

•Benchmarking — LIBERO, real-world success rates

Hands-on lab

Collect teleop demonstrations, fine-tune an open VLA policy for a sorting task and compare against an RL baseline.

Failure scenario you solve

The policy generalises to new objects but freezes when a human walks into frame — it never saw one in training.

You ship

Demonstration datasetFine-tuned VLA policyPolicy benchmark

Assessed outcome: Train and evaluate a learned robot skill and explain when it should not be trusted.

Edge Deployment, Agentic Robots & Safety

Physical AI

Put it on the machine: real-time inference, agentic task planning and the safety case that lets it deploy.

Topics

•Edge compute — Jetson Orin / Thor, Qualcomm, TensorRT

•Real-time constraints, latency budgets and watchdogs

•LLM / VLM task planners commanding robot skills

•Fleet orchestration and integration with enterprise systems

•Functional safety — ISO 10218, ISO 13482, ISO/TS 15066, risk assessment

•Human-robot interaction, override and incident response

Hands-on lab

Deploy the perception + policy stack on an edge device, wire an LLM task planner with a safety envelope, and write the safety case.

Failure scenario you solve

A voice command 'clear the table' is executed with a knife in the gripper — no safety envelope on the planner's actions.

You ship

Edge deploymentAgentic task plannerSafety case document

Assessed outcome: Deploy an agentic robot with real-time guarantees and a defensible safety case.

Capstone: Sim-to-Real Physical AI System

Capstone

One system through all four waves — predictive perception, generative interfaces, agentic planning and a robot acting in the world.

Topics

•Problem selection and success criteria with a review panel

•Perception, forecasting and anomaly detection layer

•Natural-language interface and grounded RAG for operators

•Agentic planner with guardrails and approvals

•Learned skill trained in simulation, deployed to hardware

•Evaluation evidence, safety case and demo day defence

Hands-on lab

Build and defend a complete Physical AI system — e.g. an autonomous inventory robot or an inspection arm — from simulation to a live demo before industry reviewers.

Failure scenario you solve

Everything works separately; integrated, the planner issues a command the perception stack cannot verify — you must design the contract between layers.

You ship

Deployed systemEvaluation reportSafety caseDemo video & repo

Assessed outcome: Present a working, evaluated, safety-cased Physical AI system and defend every design choice.

Honest scope — modules 1–4 build predictive ML, 5–7 generative AI, 8–10 agentic systems and 11–15 robotics and Physical AI; module 16 integrates all four into one deployed system. Hardware labs run on simulation plus shared Jetson and robot-arm kits; you do not need to buy a robot.

Tools you'll master

The full-stack AI toolkit, notebook to robot.

Real-time projects

You don't watch videos. You ship machines.

Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.

Hero project

Predictive maintenance & vision QA system

Forecast equipment failures from sensor telemetry and catch visual defects with an edge-deployed detector — trained, monitored and served with drift alarms and a latency budget.

PyTorchComputer VisionMLOpsTensorRT

Enterprise

Grounded operations copilot with agentic actions

A multimodal RAG copilot over manuals and incident images that plans maintenance responses through MCP tools — red-teamed, guardrailed and shipped with an evaluation and governance pack.

RAGLangGraphMCPGuardrails

Physical AI

Sim-to-real pick-and-sort robot

Build a digital twin in Isaac Sim, generate synthetic data, fine-tune a VLA policy on teleop demos and deploy it to a Jetson-driven arm with a safety envelope and measured success rate.

ROS 2Isaac SimVLAJetson

Project

Your Physical AI capstone, defended before industry reviewers.

Pick a real-world problem — inventory robot, inspection arm, autonomous cart. Carry it through all four waves — perception, language interface, agentic planner, learned skill — to a live demo with an evaluation report and safety case.

Download the real world project

Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.

Production-style capstoneCareer support included

Your instructor

Taught by engineers who shipped agentic AI to production.

Manikanta Kona

Founder, RoboEdify · Enterprise AI Architect

Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI

"Predictive, generative and agentic AI were the rehearsal. Physical AI is the performance — a model that acts in the world has to be right, fast and safe at the same time, and that is the engineer we train."

15 yrs

ENTERPRISE AI

2,400+

LEARNERS

4.9 /5

RATING

Manikanta is the founder of RoboEdify and brings 15 years of enterprise platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led enterprise platform and AI rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production agentic-AI deployments that replaced manual triage with autonomous, governed case-handling.

His classes get you two things other programs don't give you: a founding architect who has shipped enterprise AI from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about VLA policies, MCP tool servers or sim-to-real transfer, you have already built it. M.S. in Engineering, Purdue University.

Ravi Krishna

Chief Technologist, RoboEdify · Implementation & Delivery Lead

ROS 2 · Simulation · Edge Deployment · Safety Cases · Delivery Governance

"Implementations don't fail in configuration — they fail in discovery. Workshops that surface the real process, requirements developers build without rework, and UAT that proves it: that's what I teach."

10 yrs

IMPLEMENTATION & DELIVERY

1,800+

LEARNERS

4.8 /5

RATING

Ravi is Chief Technologist at RoboEdify, where he leads the implementation and delivery practice. After years running enterprise automation programs, he now teaches the deployment craft — simulation-first builds, hardware bring-up, evaluation evidence and safety cases that stand up in front of a review board.

His delivery modules are built from real engagement post-mortems, not slide decks. Expect to leave with working workshop kits, requirement and UAT templates, and a delivery-governance playbook you can run on day one.

HIRING PARTNERS · INDUSTRY VOICES

What AI & robotics employers say about RoboEdify grads.

Real feedback from talent leaders at the firms hiring our AI, agent and robotics graduates.

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta, Partner Programme Lead, Microsoft

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma, Senior Manager, Deloitte

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt, Solutions Lead, Mphasis

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai, Senior Architect, TCS

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon, Practice Lead, Accenture

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer, Director, Infosys

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair, VP Engineering, Wipro

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian, Engineering Director, Cognizant

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi, Practice Director, Capgemini

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma, Talent Director, IBM

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai, Practice Head, LTIMindtree

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer, Senior Manager, Tech Mahindra

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai, Talent Acquisition Lead, Cyient

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy, Talent Lead, Microsoft

Program certifications

An Agent‑Ready credential, not a participation trophy.

RoboEdify · Institute Certificate

Agent‑Ready Physical AI Engineer

Presented to

Spandana Bala

For successfully designing, training and deploying an end-to-end Physical AI system — predictive perception, grounded generative interface, guardrailed agentic planner and a learned robot skill transferred from simulation to hardware — evaluated against the 2026 Agent‑Ready rubric and safety-case standards.

Manikanta Kona

CEO · RoboEdify

AGENT
READY
2026

Industry‑recognized

Mapped to the NVIDIA Deep Learning Institute, ROS 2 and cloud ML certification paths and reviewed by robotics and AI hiring partners — the skills hiring managers already scan for on resumes.

Project artifact included

Every certificate carries your capstone name, the reviewing partner, and links to the demo video, repository and evaluation report — proof, not a promise.

Enhanced skill validation

Graded against the 2026 Agent‑Ready rubric: predictive modelling, LLM engineering, agent design, robot learning, edge deployment and safety. No pass/fail — a level 1‑5 band.

Verifiable on a public URL

Each credential has a public verification page recruiters can check in 10 seconds — no PDF back‑and‑forth.

Job roles

Roles this program prepares you for.

Physical AI Engineer Build perception, learning and control stacks for robots and autonomous machines.

Robotics Software Engineer (ROS 2) Own sensing, navigation, manipulation and integration on real robots.

Machine Learning Engineer Train, deploy and monitor predictive and vision models in production.

Robot Learning / VLA Researcher-Engineer Fine-tune and evaluate imitation, RL and vision-language-action policies.

Simulation & Synthetic Data Engineer Build digital twins and generate training data in Isaac Sim and Omniverse.

Generative AI Engineer Design RAG, fine-tuning and multimodal systems with rigorous evaluation.

AI Agent Engineer Build guardrailed, observable agents and multi-agent orchestration.

Edge AI / Embedded ML Engineer Optimise and deploy models on Jetson-class hardware under real-time limits.

Autonomous Systems Engineer Integrate perception, planning and control for vehicles, drones and AMRs.

AI Safety & Assurance Engineer Own evaluation evidence, risk assessment and safety cases for deployed AI.

What employers should see in your portfolio: a model you trained and monitored, an LLM system you evaluated, an agent you red-teamed, and a robot skill you trained in simulation and ran on hardware — with the safety case that let it deploy.

Job placement support

Your first AI engineering offer isn't a lottery ticket. It's a built process.

GitHub, LinkedIn, resume — and most importantly, warm intros into AI, robotics and enterprise hiring partners. Our placement team works your search like an account, not a helpdesk.

01 / PORTFOLIO

A portfolio, not a graveyard.

Guidance on assembling a consulting portfolio — process maps, workshop artifacts, backlog and UAT evidence, and your AI rollout plan — reviewed 1:1, not via template.

02 / RESUME PREP

Rewrite, don't proofread.

A one-page resume rebuilt around the models you shipped, the agent you deployed, and the business outcome. Reviewed by engineers who've read 10,000+ resumes.

03 / LINKEDIN + INTROS

Where most opportunities actually live.

Profile tuning plus direct warm introductions into our hiring-partner network — Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data, Capgemini. You leave with recruiter contacts, not a generic "good luck."

AI alumni

Hundreds of AI careers launched — here are eight.

Spandana Bala

ML Engineer

Hyderabad · India

Now at · Infosys

Naveen Vedala

AI Agent Engineer

Hyderabad · India

Now at · TCS

Tejashwini Addla

Simulation Engineer

Hyderabad · India

Now at · Deloitte

Tharunesh Dillikar

Robotics Software Engineer

Seattle · United States

Now at · Accenture

Mujahed Mohammed

Edge AI Engineer

Hyderabad · India

Now at · Accenture

Bhargav Kumar Murala

Physical AI Engineer

Hyderabad · India

Now at · Capgemini

Sai Manasa Leburi

Autonomous Systems Engineer

New York · United States

Now at · NTT Data

Rahul Dhamma

Robot Learning Engineer

Hyderabad · India

Now at · Cognizant

Our locations

Come chat with us — over coffee, or over Zoom.

One flagship campus in Hyderabad, plus online Physical AI classes running on Indian and US timezones.

Flagship campus

Hyderabad

2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana

Call

+91 8142998866

US desk

+1 256 388 7766

Hours

Mon–Sun · 7 AM–9 PM

Online class

Global

Weekend and evening Physical AI classes running on IST and PST. Every online class ships the same three production projects and sim-to-real capstone as the on‑campus track, with remote hardware-bench access.

Timezones

IST & PST

Format

Live + 1:1 mentorship

Admissions

ENROLLING NOW

FAQ

Questions we actually get — answered honestly.

Straight answers on prerequisites, hardware, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.

Do I need a robotics or CS background?+

No. About 40% of every class comes from mechanical, electrical and non-CS streams. You need basic Python and school-level math; module 1 rebuilds the rest, and robotics starts in simulation, so no hardware experience is assumed.

Do I need to buy a robot or a GPU?+

No. Labs run on cloud GPUs and NVIDIA Isaac Sim; hardware modules use shared Jetson kits and robot arms at the Hyderabad campus or via remote lab access. Online learners deploy to a remote hardware bench with a live camera feed.

Which models and tools will I actually build with?+

Predictive: PyTorch, scikit-learn, TensorRT. Generative: GPT, Claude, Gemini, Llama, Hugging Face, vector DBs. Agentic: LangGraph, MCP, OpenAI Agents SDK. Physical: ROS 2, Isaac Sim / Lab, Cosmos, MuJoCo, GR00T N1, π0 / OpenVLA, Jetson Orin.

Which certifications does this prepare me for?+

The curriculum maps to the NVIDIA Deep Learning Institute tracks (Deep Learning, Generative AI, Robotics with Isaac), ROS 2 developer paths, and cloud ML engineer certifications (AWS / Google / Azure). Our own Agent‑Ready credential adds graded, verifiable project evidence.

How is the learning workload structured?+

The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.

Is placement support really 1:1, and which companies hire?+

Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location, or timeline.

Online, weekend, or on-campus?+

All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same three projects and the same capstone — only the schedule and hardware-lab access model change.

What if I fall behind, or can't continue mid-class?+

Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

Still have a question? Talk to an advisor — no slides, no pitch.

One million AI‑native professionals by 2027.
Let's put you in that number.

Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.

Plan your learning

Course: Physical AI Engineer
Preparation: Diagnostic-based preparation before the common core
Level: Specialist
Curriculum: 16 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.

Physical AI Engineer

What is Physical AI — and why learn all four waves?

AI left the screen. The jobs followed it.

Built for engineers who want AI to act in the real world.

Take a model from notebook to a machine — safely.

Sixteen modules. Predictive → Generative → Agentic → Physical AI.

Python, Math & Data for AI

Machine Learning Foundations

Deep Learning & Computer Vision

Time Series, Forecasting & MLOps

LLM Foundations & Prompt Engineering

RAG, Embeddings & Knowledge Systems

Fine-tuning, Small Models & Multimodal GenAI

AI Agents: Tools, MCP & Planning

Multi-Agent Systems & Orchestration

Agent Evaluation, Guardrails & Governance

Robotics Foundations & ROS 2

Perception, SLAM & Scene Understanding

Simulation, Synthetic Data & Digital Twins

Robot Learning: RL, Imitation & VLA Policies

Edge Deployment, Agentic Robots & Safety

Capstone: Sim-to-Real Physical AI System

The full-stack AI toolkit, notebook to robot.

You don't watch videos. You ship machines.

Predictive maintenance & vision QA system

Grounded operations copilot with agentic actions

Sim-to-real pick-and-sort robot

Your Physical AI capstone, defended before industry reviewers.

Taught by engineers who shipped agentic AI to production.

What AI & robotics employers say about RoboEdify grads.

An Agent‑Ready credential, not a participation trophy.

Roles this program prepares you for.

Your first AI engineering offer isn't a lottery ticket. It's a built process.

A portfolio, not a graveyard.

Rewrite, don't proofread.

Where most opportunities actually live.

Hundreds of AI careers launched — here are eight.

Come chat with us — over coffee, or over Zoom.

Questions we actually get — answered honestly.

One million AI‑native professionals by 2027.Let's put you in that number.

Plan your learning

One million AI‑native professionals by 2027.
Let's put you in that number.