Computer Vision · Deep → 3D → Multimodal → Edge · Class 001 Enrolling Now

Vision AI Engineer

A career-focused, hands-on program in computer vision: from classical geometry and deep detection to 3D perception, vision-language models, generative and agentic vision — deployed on real cameras and edge hardware with the monitoring and safety evidence production demands.

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 does a Vision AI Engineer do?

They build systems that see: turning camera, depth and video streams into detections, measurements, 3D understanding and decisions — and keep them accurate on real hardware, in real lighting, for years. The role now spans classical geometry, deep detection and segmentation, vision-language and generative models, and agentic systems that act on what they see. This program builds that stack in order and ends with a system deployed on an edge device with the evidence to prove it works.

The vision engineering chain From pixels to a deployed decision — one owner.

01PERCEIVE

•Cameras, calibration & geometry
•Classical CV & feature pipelines
•Detection, segmentation, tracking
•Depth, point clouds & 3D

02UNDERSTAND

•Vision-language models & CLIP
•Open-vocabulary detection
•Visual QA & document AI
•Multimodal retrieval

03CREATE & ACT

•Diffusion & synthetic data
•Digital twins & simulation
•Vision agents with MCP tools
•Guardrails & human approval

04DEPLOY

•TensorRT, ONNX & Jetson
•Multi-stream video pipelines
•Drift monitoring & data engines
•Safety, privacy & compliance

What a Vision AI engineer owns

•Datasets that reflect the real camera, lighting and edge cases — not the benchmark
•Models that meet accuracy, latency and power budgets on the target hardware
•Data engines that catch drift and retrain before quality falls
•Bias, privacy and safety evidence before a camera goes live

And remaining accountable for every decision the system makes from an image.

Vision AI landscape · 2026

Vision became multimodal. The job description changed with it.

Vision foundation modelsSAM 2 · DINOv2 · Depth Anything

Promptable segmentation, self-supervised features and zero-shot depth mean most projects now start from a foundation model and fine-tune — labelling budgets fall, evaluation discipline matters more.

Vision-language modelsGPT-4o · Gemini · Qwen-VL

Models that read images, video and documents in natural language power open-vocabulary detection, visual QA and document AI — engineers must ground them and measure hallucination, not just prompt them.

Real-time detection & trackingYOLO · RT-DETR · ByteTrack

Transformer detectors now match YOLO speed on edge GPUs; multi-object tracking and re-identification underpin safety, retail and logistics analytics at scale.

3D & spatial AIGaussian splatting · NeRF · SLAM

Fast 3D reconstruction, metric depth and 6-DoF pose feed robots, AR glasses and digital twins — 3D vision moved from research to product requirement.

Generative & synthetic visionDiffusion · ControlNet · Cosmos

Controllable image and video generation plus photoreal simulation supply the rare-defect and edge-case data real cameras never capture — when validated against real-world lift.

Edge inference & regulationJetson Thor · EU AI Act

Vision runs on-device for latency, cost and privacy; face-recognition law, biometric rules and the EU AI Act decide what can deploy — evidence, not demos, gets a camera signed off.

What this means for your career: employers want engineers who can carry a vision model from dataset to a camera in production — fine-tune foundation models, deploy on the edge, monitor drift and defend the safety and privacy case. That combined profile is scarce and commands a premium over notebook-only ML.

Who should join

Built for engineers who want machines to see.

Software engineers moving into AI Data scientists & ML engineers ECE, mechanical & mechatronics graduates Embedded & edge engineers Robotics, AV & drone engineers Manufacturing, retail & med-tech engineers

Prior experience: basic Python and school-level math. Module 1 rebuilds Python, linear algebra and image fundamentals from scratch; no prior computer-vision or GPU experience is assumed.

What you will be able to do

Take a vision model from dataset to a camera in production.

Build detection & segmentationTrain, evaluate and fine-tune YOLO, DETR, SAM 2 and ViT models with leak-free splits and honest metrics.

Understand 3D scenesDepth, point clouds, SLAM, Gaussian splatting and 6-DoF pose with quantified accuracy.

Engineer with VLMsOpen-vocabulary detection, visual QA and document AI with grounding evaluation and LoRA fine-tuning.

Generate useful synthetic dataDiffusion, ControlNet and digital twins that measurably lift real-world performance.

Deploy on the edgeTensorRT / ONNX optimisation, Jetson and DeepStream pipelines within latency and power budgets.

Operate and certifyDrift monitoring, data engines, bias and privacy evaluation and safety cases aligned to the EU AI Act.

Course curriculum

Twelve modules. Pixels → 3D → multimodal → deployed vision.

Python, Math & Image Fundamentals

Foundations

The toolkit every vision system depends on: Python for engineering, the linear algebra behind pixels and cameras, and clean image data pipelines.

Topics

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

•Linear algebra, probability and optimisation for vision

•Images as tensors — colour spaces, sampling, filtering

•Camera models, intrinsics and calibration

•Dataset collection, labelling tools and versioning

•Git, Docker and reproducible GPU environments

Hands-on lab

Calibrate a camera, build a versioned image dataset with a labelling pipeline and ship a reproducible baseline classifier.

Failure scenario you solve

Two annotators labelled the same defect two different ways — the model learned the disagreement, not the defect.

You ship

Reproducible repoCalibrated camera profileVersioned labelled dataset

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

Classical Computer Vision & Geometry

Classical Vision

The algorithms that still run most production lines — and the geometry deep models depend on.

Topics

•Edge, corner and blob detection; morphology

•Feature descriptors — SIFT, ORB — and matching

•Homographies, stereo and epipolar geometry

•Optical flow and motion estimation

•Template matching and industrial inspection classics

•When classical beats deep learning (and vice versa)

Hands-on lab

Build a stereo depth pipeline and a rule-based inspection tool for a PCB line; benchmark against a deep baseline.

Failure scenario you solve

A $50k deep-learning project replaced a 200-line OpenCV script that was faster, cheaper and more accurate.

You ship

Stereo depth pipelineInspection toolClassical vs deep report

Assessed outcome: Choose the right technique for a vision problem and justify it with measurements.

Deep Learning for Vision

Deep Vision

Neural networks from first principles to production-grade image models in PyTorch.

Topics

•PyTorch — tensors, autograd, training loops, mixed precision

•CNNs, ResNets, EfficientNets and transfer learning

•Vision Transformers (ViT, Swin) and ConvNeXt

•Data augmentation, class imbalance and regularisation

•Metrics — precision/recall, mAP, IoU, calibration

•Experiment tracking and hyper-parameter search

Hands-on lab

Train a defect classifier on an imbalanced factory dataset; deliver a calibrated model with a confusion-matrix error analysis.

Failure scenario you solve

The model scores 99% on validation because the same part appears in both splits under different lighting.

You ship

Trained classifierError-analysis reportExperiment dashboard

Assessed outcome: Train and defend an image model with the right metric and a leak-free split.

Object Detection, Segmentation & Tracking

Deep Vision

Find, outline and follow things — the core of most commercial vision products.

Topics

•Detection — YOLO family, DETR / RT-DETR, anchor-free heads

•Instance and semantic segmentation — Mask R-CNN, SAM 2

•Keypoints and pose estimation

•Multi-object tracking — ByteTrack, re-identification

•Video understanding and temporal models

•Labelling strategy, active learning and hard-negative mining

Hands-on lab

Build a people-and-forklift detection + tracking system for a warehouse feed with SAM-assisted labelling and a tracking benchmark.

Failure scenario you solve

Tracks swap IDs every time two workers cross — safety alerts fire on the wrong person.

You ship

Detector + trackerActive-learning loopMOT benchmark

Assessed outcome: Deliver a detection and tracking stack with measured mAP and tracking accuracy on real video.

3D Vision, Depth & Scene Reconstruction

3D Vision

Move from pixels to metric 3D — depth, point clouds and reconstructions robots and AR rely on.

Topics

•Depth estimation — stereo, monocular (Depth Anything), ToF / LiDAR

•Point clouds — Open3D, registration, PointNet / transformers

•Structure from motion and visual SLAM

•NeRFs and 3D Gaussian Splatting

•6-DoF object pose estimation

•Multi-sensor calibration and fusion

Hands-on lab

Reconstruct a warehouse aisle from video, estimate 6-DoF pallet poses and fuse LiDAR with camera depth.

Failure scenario you solve

Monocular depth is beautiful on the demo and 40% off in metric scale — the robot reaches into empty air.

You ship

3D reconstructionPose-estimation nodeFusion benchmark

Assessed outcome: Produce metric 3D scene understanding with quantified accuracy.

Vision-Language & Multimodal Models

Generative Vision

Vision that understands language — open-vocabulary detection, captioning, visual QA and document AI.

Topics

•CLIP, SigLIP and contrastive embeddings

•Open-vocabulary detection — Grounding DINO, OWL-ViT

•VLMs — GPT-4o, Gemini, Qwen-VL, LLaVA, PaliGemma

•Visual QA, captioning and visual grounding

•Document AI, OCR and layout understanding

•Fine-tuning VLMs with LoRA on domain data

Hands-on lab

Build an open-vocabulary inspection assistant: describe a defect in words, detect it, and fine-tune a small VLM on domain images.

Failure scenario you solve

The VLM confidently describes a 'scratch' on a reflection — no grounding check, no confidence threshold.

You ship

Open-vocab detectorFine-tuned VLMGrounding eval set

Assessed outcome: Deploy a multimodal model with measured grounding accuracy on domain imagery.

Generative Vision & Synthetic Data

Generative Vision

Create images and video on purpose — for augmentation, simulation and product features.

Topics

•Diffusion models — Stable Diffusion, FLUX, ControlNet

•Image editing, inpainting and style transfer

•Video generation and world models (Cosmos, Veo, Sora)

•Synthetic data for rare defects and edge cases

•Photoreal simulation — Isaac Sim / Omniverse, domain randomisation

•Detecting generated content and provenance

Hands-on lab

Generate 20k synthetic rare-defect images with ControlNet and a digital twin; measure the real-world lift on the module 3 classifier.

Failure scenario you solve

Synthetic data doubled the dataset and reduced accuracy — the render engine never reproduced the real sensor noise.

You ship

Synthetic datasetGeneration pipelineReal-data lift report

Assessed outcome: Use generative models to measurably improve a vision system, not just to make pictures.

Vision Agents & Multimodal RAG

Agentic Vision

Vision inside agent loops — systems that look, retrieve, reason and act.

Topics

•Agent loops with visual tools — detect, crop, zoom, measure

•Model Context Protocol (MCP) tool servers for vision

•Multimodal RAG over images, diagrams and video frames

•Computer-use and GUI agents

•Video analytics agents — event detection and summarisation

•Evaluation, guardrails and human-in-the-loop for vision agents

Hands-on lab

Build an inspection agent that reviews camera frames via MCP tools, retrieves the matching spec drawing and raises an approved work order.

Failure scenario you solve

The agent escalates 400 false alarms overnight because the detector threshold was never part of its policy.

You ship

Vision MCP serverInspection agentAgent eval report

Assessed outcome: Design a vision agent whose actions are bounded, traceable and evaluated.

Edge Deployment & Real-Time Optimisation

Deployment

Make vision run where the camera is — fast, small and reliable.

Topics

•Quantisation, pruning and distillation

•ONNX, TensorRT, OpenVINO and CoreML export

•NVIDIA Jetson, Qualcomm, Hailo and mobile GPUs

•DeepStream and GStreamer video pipelines

•Latency, throughput and power budgets

•Camera integration — GigE, RTSP, industrial protocols

Hands-on lab

Deploy the module 4 detector on a Jetson at 30 FPS across four RTSP streams within a 15 W power budget.

Failure scenario you solve

Cloud inference hits 95% accuracy at 900 ms — the conveyor moves 2 m in that time.

You ship

Edge-optimised modelMulti-stream pipelineLatency & power benchmark

Assessed outcome: Meet an accuracy, latency and power budget on real edge hardware.

Vision MLOps, Monitoring & Data Engines

Deployment

Operate vision systems for years, not demos for a day.

Topics

•Model serving — Triton, TorchServe, batch vs streaming

•Data drift, lighting drift and camera-change detection

•Data engines — collect, label, retrain, redeploy loops

•Model registries, A/B and shadow deployments

•Annotation quality and dataset health metrics

•Cost engineering for video at scale

Hands-on lab

Build a data engine that detects drift on a live camera, mines hard frames for labelling and retrains on a schedule.

Failure scenario you solve

A seasonal lighting change silently cut recall by half for three months — nobody monitored anything but uptime.

You ship

Serving stackDrift dashboardAutomated retraining loop

Assessed outcome: Operate a vision model in production with measured, alerted quality.

Domain Applications, Safety & Ethics

Deployment

Where Vision AI is hired: the domains, their constraints and the rules that govern them.

Topics

•Industrial inspection and quality control

•Retail, logistics and warehouse analytics

•Medical imaging fundamentals and regulation

•Autonomous vehicles, drones and robotics perception

•Privacy, bias, face-recognition law and the EU AI Act

•Safety cases and failure-mode analysis for vision systems

Hands-on lab

Write a deployment and safety assessment for a people-detection system: bias evaluation across conditions, privacy design and a risk register.

Failure scenario you solve

A retail analytics system is pulled after launch — nobody checked performance across skin tones or consulted privacy law.

You ship

Bias evaluationPrivacy designRisk register & safety case

Assessed outcome: Take a vision system to deployment with evidence a compliance team will sign off.

Capstone: Production Vision AI System

Capstone

One end-to-end system — data engine, models, edge deployment, agentic actions and a safety case — defended before industry reviewers.

Topics

•Problem selection and success criteria with a review panel

•Data engine, labelling and synthetic augmentation

•Detection / segmentation / 3D models with benchmarks

•Multimodal or agentic layer where it adds value

•Edge deployment with latency and power evidence

•Monitoring, safety case and demo-day defence

Hands-on lab

Build and defend a complete Vision AI system — e.g. automated inspection cell, warehouse safety monitor or retail shelf analytics — from dataset to live edge demo.

Failure scenario you solve

Every component works alone; integrated, the tracker feeds the agent stale IDs — you must design the contract between layers.

You ship

Deployed systemBenchmark reportSafety caseDemo video & repo

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

Honest scope — modules 1–2 build foundations and classical vision, 3–5 deep and 3D vision, 6–8 multimodal, generative and agentic vision, 9–11 edge deployment, MLOps and domain safety; module 12 integrates everything into one deployed system. Labs run on cloud GPUs plus shared Jetson and camera kits; you do not need to buy hardware.

Tools you'll master

The vision engineer's toolkit, notebook to camera.

Real-time projects

You don't watch videos. You ship cameras.

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

Hero project

Industrial defect-inspection cell

Train a segmentation-based defect detector on an imbalanced factory dataset, boost rare classes with ControlNet synthetic data, and deploy on a Jetson at line speed with drift monitoring.

PyTorchSAM 2Synthetic DataTensorRT

Enterprise

Warehouse safety & analytics monitor

Multi-camera people-and-forklift detection with tracking and re-identification, zone-based safety alerts, a bias evaluation across conditions and a privacy-by-design assessment.

RT-DETRByteTrackDeepStreamPrivacy

Multimodal

Vision-language inspection agent

An agent that reviews frames through MCP vision tools, grounds findings in spec drawings with multimodal RAG, and raises approved work orders — with a fine-tuned VLM, grounding evals and guardrails.

VLMMCPMultimodal RAGGuardrails

Project

Your Vision AI capstone, defended before industry reviewers.

Pick a real-world problem — inspection cell, shelf analytics, drone survey, medical-image triage. Carry it from data engine to edge deployment — models, 3D or multimodal layer, monitoring — to a live demo with a benchmark 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 · Computer Vision · Multimodal Models · Edge AI

"A vision model that works on the benchmark is the start. One that works on the customer's camera, in their lighting, a year later — 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 SAM 2 fine-tuning, TensorRT deployment or VLM grounding, you have already built it. M.S. in Engineering, Purdue University.

Ravi Krishna

Chief Technologist, RoboEdify · Implementation & Delivery Lead

Camera Systems · Edge Deployment · Vision MLOps · Safety Cases

"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 — camera integration, edge deployment, 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 vision & AI employers say about RoboEdify grads.

Real feedback from talent leaders at the firms hiring our vision and AI 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 vision track produces engineers who ship production-grade perception code on day one. Genuinely rare.

Deepak Pillai, Senior Architect, TCS

What sets RoboEdify apart is the edge-deployment layer baked into the vision 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 computer-vision 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 vision 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 Vision AI Engineer

Presented to

Spandana Bala

For successfully designing, training and deploying an end-to-end Vision AI system — data engine, detection and 3D models, multimodal or agentic layer and edge deployment with monitoring — 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 computer-vision tracks, TensorFlow / PyTorch and cloud ML certification paths and reviewed by vision 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: data engineering, model training, 3D and multimodal vision, edge deployment, monitoring 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.

Computer Vision Engineer Build detection, segmentation, tracking and 3D perception for real products.

Vision AI / Applied ML Engineer Fine-tune vision foundation models and ship them with rigorous evaluation.

Perception Engineer (Robotics / AV) Own camera, LiDAR and depth pipelines for robots, drones and vehicles.

Edge AI Engineer Optimise and deploy vision models on Jetson-class hardware in real time.

Multimodal AI Engineer Build VLM, visual QA and document-AI systems with grounding evaluation.

3D Vision / Spatial AI Engineer Reconstruction, SLAM, Gaussian splatting and pose for AR and digital twins.

Video Analytics Engineer Multi-stream detection, tracking and event analytics at scale.

Synthetic Data / Simulation Engineer Generate and validate synthetic imagery with diffusion and digital twins.

Medical / Industrial Imaging Engineer Apply vision under regulated, high-stakes domain constraints.

ML Platform Engineer (Vision MLOps) Run data engines, drift monitoring and retraining loops for vision fleets.

What employers should see in your portfolio: a detector you trained on messy real data, a 3D or multimodal system you evaluated honestly, a model running on an edge device within budget, and the monitoring and safety evidence that let a camera go live.

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

Video Analytics Engineer

Hyderabad · India

Now at · Deloitte

Tharunesh Dillikar

Computer Vision Engineer

Seattle · United States

Now at · Accenture

Mujahed Mohammed

Edge AI Engineer

Hyderabad · India

Now at · Accenture

Bhargav Kumar Murala

Vision AI Engineer

Hyderabad · India

Now at · Capgemini

Sai Manasa Leburi

3D Vision Engineer

New York · United States

Now at · NTT Data

Rahul Dhamma

Perception Engineer

Hyderabad · India

Now at · Cognizant

Our locations

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

One flagship campus in Hyderabad, plus online Vision 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 Vision AI classes running on IST and PST. Every online class ships the same three production projects and deployed-vision 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 computer-vision or CS background?+

No. About 40% of every class comes from ECE, mechanical and non-CS streams. You need basic Python and school-level math; module 1 rebuilds linear algebra, image fundamentals and camera geometry from scratch.

Do I need a GPU or camera hardware?+

No. Training runs on cloud GPUs; edge modules use shared Jetson kits and industrial cameras at the Hyderabad campus or via remote lab access with live RTSP streams. A laptop webcam is enough for the early labs.

Which models and tools will I actually build with?+

Classical: OpenCV, Open3D. Deep: PyTorch, YOLO, RT-DETR, SAM 2, DINOv2, ViT. Multimodal & generative: CLIP, Grounding DINO, GPT-4o / Gemini / Qwen-VL, Stable Diffusion, ControlNet, Isaac Sim. Deployment: TensorRT, ONNX, OpenVINO, DeepStream, Triton, Jetson Orin.

Which certifications does this prepare me for?+

The curriculum maps to the NVIDIA Deep Learning Institute computer-vision and edge tracks, the TensorFlow / PyTorch 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: Vision AI Engineer
Preparation: Diagnostic-based preparation before the common core
Level: Specialist
Curriculum: 12 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.

Vision AI Engineer

What does a Vision AI Engineer do?

Vision became multimodal. The job description changed with it.

Built for engineers who want machines to see.

Take a vision model from dataset to a camera in production.

Twelve modules. Pixels → 3D → multimodal → deployed vision.

Python, Math & Image Fundamentals

Classical Computer Vision & Geometry

Deep Learning for Vision

Object Detection, Segmentation & Tracking

3D Vision, Depth & Scene Reconstruction

Vision-Language & Multimodal Models

Generative Vision & Synthetic Data

Vision Agents & Multimodal RAG

Edge Deployment & Real-Time Optimisation

Vision MLOps, Monitoring & Data Engines

Domain Applications, Safety & Ethics

Capstone: Production Vision AI System

The vision engineer's toolkit, notebook to camera.

You don't watch videos. You ship cameras.

Industrial defect-inspection cell

Warehouse safety & analytics monitor

Vision-language inspection agent

Your Vision AI capstone, defended before industry reviewers.

Taught by engineers who shipped agentic AI to production.

What vision & AI 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.