AI Engineering · Practical career preparation

Generative AI Engineer

LLM Applications, RAG & Evaluation

Build AI applications whose answers you can evaluate and improve. Develop applications with language models, structured outputs and retrieval. Build a source-grounded assistant, measure its failures and compare prompting, retrieval and a bounded model-adaptation exercise.

12 core modules Diagnostic-based preparation Three guided projects One independently assessed capstone
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What does this engineering pathway involve?

A generative AI engineer builds applications around models that generate text or other content. The engineering work includes data access, interfaces, evaluation, latency, cost and failure handling. This course concentrates on a grounded document application, with multimodal and model-adaptation practice kept within a defined scope.

Learning approach

Build systems you can inspect, test and explain.

Each practical task includes an explicit requirement, a baseline and a failure investigation. You keep versions and results so another learner can reproduce your work. The final assessment includes an unfamiliar debugging exercise as well as your prepared project.

Learning format

Learn through classes, projects and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and course-specific software/compute access before enrollment.

On-campusLive onlineWorking-professional format
Who should join

A common core, with preparation matched to your starting point.

Graduates and working engineers should have Python, basic APIs, Git and AI-evaluation readiness. Foundations of Engineering Computing and Foundations of AI provide suitable starting points, or you can demonstrate equivalent skills. Prior experience building an LLM application is not required.

A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.

Learning outcomes

Leave with skills you can demonstrate.

Define an LLM taskBuild a reference set and choose a useful baseline.
Validate generated outputsUse schemas and handle invalid or incomplete responses.
Build retrieval and groundingPreserve document identity, versions and access rules.
Measure quality and failureEvaluate retrieval, answers, abstention and regressions separately.
Compare adaptation choicesExplain when prompting, retrieval or limited fine-tuning is justified.
Deliver a reproducible applicationDocument service behavior, cost, latency and limitations.
Readiness areas

What you should be able to do before you start.

Python and APIsFunctions, exceptions, JSON, HTTP requests and source control.
Data and AI conceptsTrain/evaluation separation, basic model behavior and meaningful metrics.
Application basicsUnderstand input validation, user identity and configuration.

A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.

Course curriculum

Twelve modules, from task definition to an independent capstone.

Every module includes topics, a practical task, a failure to investigate, deliverables and an assessed outcome.

01

LLM application architecture and task definition

Engineering core
+
Topics
•Use-case boundaries
•Input/output contracts
•Model versus application responsibilities
•Deterministic baseline
•Reference questions
Hands-on lab
Define a document-assistant task and a frozen set of answerable and unanswerable questions.
Failure scenario you solve
A convincing demo has no definition of a correct answer.
You build
Task specification and evaluation cases.
Assessed outcome: Explain what the application must answer and when it should abstain.
02

Model behavior and selection

Engineering core
+
Topics
•Tokens/context
•Probabilistic outputs
•Inference settings
•Model versions
•Closed/local model tradeoffs
•Cost/latency
Hands-on lab
Compare selected models or configurations on the same bounded test set.
Failure scenario you solve
A model change improves examples while degrading another scenario class.
You build
Model comparison and configuration manifest.
Assessed outcome: Choose a model based on observed quality and operating constraints.
03

Prompting and structured generation

Engineering core
+
Topics
•Instruction design
•Few-shot examples
•Schemas
•Validation
•Refusals
•Truncation
•Bounded repair
Hands-on lab
Generate typed records and reject or repair invalid responses through an explicit workflow.
Failure scenario you solve
A syntactically valid response contains an invalid field value.
You build
Prompt versions, schema and validation tests.
Assessed outcome: Separate parse validity from semantic correctness.
04

Document ingestion and access-aware indexing

Engineering core
+
Topics
•Parsing
•Chunking
•Metadata
•Versions
•Provenance
•Document permissions
•Duplicate handling
Hands-on lab
Index a small supplied corpus and retain document identity, revision and access metadata.
Failure scenario you solve
An old document revision is treated as current policy.
You build
Ingestion pipeline and corpus manifest.
Assessed outcome: Trace a retrieved passage to its source and permitted audience.
05

Retrieval and ranking

Engineering core
+
Topics
•Embeddings
•Lexical/vector search
•Hybrid retrieval
•Reranking
•Query design
•Retrieval metrics
Hands-on lab
Compare a simple baseline and one retrieval improvement on a fixed relevance set.
Failure scenario you solve
The generator is blamed for an answer whose supporting passage was never retrieved.
You build
Retrieval pipeline and relevance evaluation.
Assessed outcome: Distinguish retrieval failure from generation failure.
06

Grounded answers and abstention

Engineering core
+
Topics
•Evidence selection
•Citation mapping
•Support checks
•Conflicting documents
•Unanswerable questions
•Source freshness
Hands-on lab
Build answers with source references and defined abstention when evidence is insufficient.
Failure scenario you solve
A citation points to a real document but does not support the stated claim.
You build
Grounded-answer pipeline and support checks.
Assessed outcome: Demonstrate citation correctness and justified abstention on held-out questions.
07

Evaluation and regression engineering

Engineering core
+
Topics
•Golden/reference sets
•Human rubrics
•Model-as-judge limits
•Repeated trials
•Quality slices
•Regression tracking
Hands-on lab
Create a repeatable evaluation harness and review disagreements between automated and human assessment.
Failure scenario you solve
A judge gives high scores to fluent but unsupported answers.
You build
Evaluation harness, failure taxonomy and review report.
Assessed outcome: Explain metric limitations and reproduce a regression.
08

Application integration and operating behavior

Engineering core
+
Topics
•API/service boundary
•Authentication
•Authorization
•Streaming/cancellation
•Timeouts
•Rate limits
•Cache isolation
•Logs
Hands-on lab
Build a small authenticated demo and test timeout, cancellation and cross-user data isolation.
Failure scenario you solve
A cached answer exposes another user’s restricted document.
You build
Application, integration tests and operating contract.
Assessed outcome: Show valid user access and defined failure behavior.
09

Multimodal extraction and document understanding

Engineering core
+
Topics
•Images/tables
•OCR or visual extraction
•Structured fields
•Provenance
•Validation
•Modality limits
Hands-on lab
Extract a bounded set of fields from supplied document images and compare with references.
Failure scenario you solve
A table column is shifted, producing plausible but incorrect values.
You build
Extraction workflow and field-level error report.
Assessed outcome: Validate extracted information instead of treating model output as ground truth.
10

Model adaptation and security review

Engineering core
+
Topics
•Prompt/RAG/adaptation choices
•LoRA/PEFT concepts
•Licensed small model
•Holdouts
•Prompt injection
•Secret/data boundaries
Hands-on lab
Run a bounded supplied adapter-training recipe and compare with an unadapted baseline; test hostile document instructions separately.
Failure scenario you solve
Adaptation improves seen examples but regresses held-out cases, or retrieved text redirects the application.
You build
Adaptation comparison, model/data manifest and adversarial tests.
Assessed outcome: Justify whether adaptation helped and identify remaining application risks.
11

Independent grounded-assistant capstone

Capstone
+
Topics
•Frozen corpus and roles
•Retrieval
•Answers
•Abstention
•Evaluation
•Service failures
•Handoff
Hands-on lab
Deliver a document assistant with source-linked answers and a held-out evaluation suite.
Failure scenario you solve
The assistant answers confidently from a document the user should not access.
You build
Application repository, evaluation results and operating guide.
Assessed outcome: Demonstrate required quality/access/failure cases and explain limitations.
12

Technical interviews and portfolio defense

Career preparation
+
Topics
•Architecture walkthrough
•Retrieval diagnosis
•Evaluation interpretation
•Cost/latency
•Personal contribution
Hands-on lab
Diagnose an unfamiliar retrieval, citation or validation failure and defend the capstone.
Failure scenario you solve
A learner can describe RAG but cannot explain the origin of a wrong answer.
You build
Technical case study and individual assessment.
Assessed outcome: Trace the failure across the pipeline and propose a testable correction.

Scope note: Core depth is an evaluated LLM application. Autonomous action execution, extensive multi-agent orchestration and large-scale post-training are separate specializations. A RAG demo is not a promise of error-free answers.

Tools and methods

Work with a focused, qualified stack.

Python and service frameworkBuild validated application interfaces.
Approved model API or local runtimeRun controlled generation and compare configurations.
Retrieval/index toolsImplement one qualified lexical/vector retrieval stack.
Evaluation scripts and tracingInspect quality, errors, latency and usage.
Hugging Face PEFT / small-model toolingComplete the bounded adaptation exercise.

The primary model, retrieval stack and permitted API/compute budget are specified before enrollment. The small-model adaptation lab uses a qualified recipe and appropriate compute access; no large-model training claim is made. Provider names are not certifications or partnership claims.

Projects and portfolio

Three guided projects and one independent capstone.

The guided projects develop across the modules and are integrated into the core.

Guided project 1

Structured-output workflow

Generate typed data with schema and semantic checks.

Portfolio evidence
Prompt versionsValidatorFailure cases
Guided project 2

Access-aware retrieval system

Index a versioned corpus and compare retrieval methods.

Portfolio evidence
Corpus manifestSearch pipelineRelevance report
Guided project 3

Model-adaptation comparison

Use a supplied small-model recipe and compare against simpler baselines.

Portfolio evidence
Adapter recipeHoldout resultsDecision memo
Capstone

A source-grounded document assistant

Build an authenticated teaching application over a small versioned corpus. Return supported answers with citations, abstain when needed and measure quality, access behavior, latency and cost across a frozen scenario set.

What you submit
  • •A frozen task specification and acceptance checklist.
  • •Source, configuration and data/input manifests.
  • •Baseline comparisons and retained evaluation results.
  • •Tests of the required failure cases.
  • •A readable handoff and individual technical defense.
What the assessor checks
  • •Keep final evaluation cases separate from tuning and retain failures.
  • •Check source support and citation accuracy; a real URL alone is not evidence of answer support.
  • •Apply user access rules before returning restricted evidence; test cross-user retrieval and cache behavior.
  • •Test unanswerable inputs, conflicting revisions, hostile document instructions, timeouts and malformed responses.
  • •Report observed quality, latency and cost with model/configuration versions and clearly stated measurement boundaries.
Assessment and completion

Demonstrate practical competence.

RoboEdify · Certificate of Completion
Generative AI Engineer
Presented to
Learner name
Awarded for completing the practical assessments and independently defending the course capstone within its documented scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
30%
Module labs
20%
Practical checkpoints
35%
Capstone
15%
Individual debugging and defense
The proposed completion standard is 70% overall, at least 60% separately in capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies gaps for remediation and reassessment. A numerical score does not compensate for missing required behavior.
This is a proposed RoboEdify course credential. External vendor certification or accreditation is not implied.
Career preparation and progression

Prepare for relevant work with evidence you can explain.

Generative AI Application Trainee / Junior EngineerBuild grounded applications and validated interfaces.
RAG / Knowledge-Application DeveloperDevelop retrieval and answer-evaluation evidence.
LLM Evaluation / Integration TraineeInvestigate regressions, service failures and model tradeoffs.

Your preparation includes a reviewed repository, a technical case study, truthful resume statements and a live debugging/interview exercise. Each employer sets its own experience and eligibility requirements.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on retrieval pipelines, source-grounded answers, evaluation results and application tests you complete.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this newly drafted course.

Microsoft logo

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

Aakash Mehta

Aakash Mehta, Partner Programme Lead, Microsoft

Deloitte logo

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

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

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

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

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

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

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

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

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

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

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

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

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

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

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

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

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

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

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

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

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

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

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

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

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

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations

Come chat with us—on campus or online.

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
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. This course uses the software and compute arrangements described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support

Support when you need to catch up.

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

FAQs

Questions about prerequisites, tools and completion.

Is this only prompt engineering?+
No. It covers data ingestion, retrieval, access controls, evaluation and application behavior as well as prompts.
Do I need to train a large language model?+
No. The core uses existing models. Adaptation is a bounded exercise with a supplied small-model recipe.
Will I need paid APIs or a GPU?+
The selected model and compute arrangement will be explained before enrollment, including any usage budget or learner costs.
How is this different from Agentic AI Engineer?+
This course builds evaluated generative applications. Agentic AI adds tool execution, durable state, authorization and recovery after actions.
Can graduates and working engineers both join?+
Yes, with the stated readiness. Both follow the same practical core; preparation is assigned through a diagnostic.
How is the learning workload structured?+
The program combines live classes, guided labs, projects and support sessions. An advisor can explain the current class format before enrollment.
Can I study online or on campus?+
RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. The course-specific software and compute setup is explained separately.
What if I need to pause or catch up?+
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support learning. Recordings remain available for the lifetime of your account.
Is placement guaranteed?+
No. Support includes portfolio/profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
What if I miss a practical requirement?+
Feedback identifies the missing capability and reassessment work. Attendance alone does not meet the completion standard. Confirm course-specific reassessment arrangements before enrollment.
How can I ask about fees and tool access?+
Contact an advisor about preparation, the current offering, fees, software/compute access and any model-usage costs before enrollment.

Still have a question?

Find your starting point in Generative AI Engineer.
One million AI-native professionals by 2027.

Tell us about your programming, data and application experience. We’ll help you understand the preparation you need and the practical projects this course is designed to develop.

Plan your learning

Course
Generative 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.