Generative AI Engineer
Course in Hyderabad
A role-focused path through the AI & ML Certification Program
This role course arranges the AI and ML programme around a generative AI engineer, who builds applications on top of large language models. You start with prompts, embeddings and retrieval, then add the backend, agent and evaluation skills that make such an application dependable for users.
- Prompt engineering
- LLM API integration
- Structured outputs
- Embeddings and pgvector
- RAG pipelines
- LangChain workflows
- LangGraph agents
- RAG evaluation
Same duration and fees as the AI / ML programme.
What a Generative AI Engineer does
A generative AI engineer builds applications where a large language model does part of the work: answering questions from documents, drafting text, extracting fields from messy files or acting as an assistant that calls other tools. The model is usually reached through an API rather than trained from scratch. Your job is everything around it: the prompt, the retrieval of the right context, the format of the output and the safeguards that stop wrong answers reaching users.
In a normal week you might improve a retrieval pipeline that returns the wrong paragraph, tighten a prompt so answers come back as valid JSON, and add a reranking step. You test changes against a set of questions with known answers, because an answer that reads well may still be wrong. You also connect the application to a database and an API layer, and check cost and response time before anything is released.
Generative AI engineers work in product companies, IT services and consulting teams, startups and support, HR, legal, finance or education teams inside larger firms that want assistants over their own documents. The role matters because language models are easy to demonstrate and hard to make dependable. Teams look for engineers who can measure answer quality, control what the model can see and do, and keep the system safe when users try unexpected inputs.
What you will be able to do
- Engineer prompts and use structured outputs and function calling with an LLM API.
- Build a document ingestion and chunking pipeline that feeds a retrieval system.
- Store embeddings in pgvector and run semantic search over a document collection.
- Build a complete RAG application that answers questions from a real document set.
- Evaluate and improve retrieval quality using reranking and a fixed set of test questions.
- Build an agent with LangGraph that calls tools and asks for human approval.
- Describe prompt injection and apply guardrails, retries and fallbacks in an application.
Who this course is for
Final-year computer science student
You can finish a working RAG application before campus hiring. A deployed assistant over real documents, with notes on how you measured it, gives interviewers something concrete to discuss.
Web or app developer
You already build features and APIs. This path shows how to add language model features properly, with retrieval, structured outputs and tests, instead of a single prompt pasted into a form.
Non-IT graduate with strong writing or domain skills
Language models reward clear thinking and good writing. Start with the Python module, then use documents from a field you know, such as HR, law or teaching, for your projects.
Data analyst or QA engineer
Your habit of checking outputs is valuable here. Lean into the evaluation, retrieval quality and regression testing topics, and use Python and APIs to turn those checks into an application.
What you will learn as a Generative AI Engineer
These are the AI / ML programme modules that matter most for this role, in the order that suits it. Every topic, tool and lab below is part of the programme syllabus.
Generative AI & LLM Application Engineering
Module 4 · 40 HrsThis module is the core of the role. Give the most time to prompts, structured outputs, chunking, embeddings and RAG, and practise judging answers rather than admiring them.
See the full module →What you study
- Large language models
- LLM APIs and prompt engineering
- Structured outputs and function calling
- Document ingestion and chunking
- Retrieval-Augmented Generation (RAG)
- Reranking and RAG evaluation
Tools you use
LLM APIsLangChainpgvectorVector DBsHugging FaceHands-on lab
Build a full Retrieval-Augmented Generation (RAG) application end to end.
Python Backend Development with AI
Module 2 · 40 HrsA language model feature needs a proper service around it. Concentrate on FastAPI, PostgreSQL, Redis job queues, login and access control, and Docker, so your application can face real users.
See the full module →What you study
- FastAPI development and RESTful API design
- PostgreSQL and MongoDB database fundamentals
- Redis and background job queues
- Authentication, authorization and JWT / OAuth
- API security and role-based access control
- Docker containerization
Tools you use
FastAPIPostgreSQLRedisDockerHands-on lab
Add background job processing using Redis-backed queues.
Agentic AI Engineering
Module 5 · 40 HrsAgents let a model use tools and follow several steps. Concentrate on tool calling, memory, human approval, guardrails and prompt injection defence, since these decide whether an agent is safe to release.
See the full module →What you study
- Tool calling and function calling
- State and memory management
- Routing and agent workflows
- Model Context Protocol (MCP)
- Human-in-the-loop design
- Prompt injection defence
Tools you use
LangGraphMCPLLM APIsVector DBsHands-on lab
Connect an agent to external tools and data sources using the Model Context Protocol.
MLOps, LLMOps & AI Platform Engineering
Module 6 · 30 HrsAnswer quality must be measured continuously. Focus on prompt versioning, AI and RAG evaluation, regression tests, tracing and cost and latency, so you can prove a change made the system better.
See the full module →What you study
- Model and prompt versioning
- AI and RAG evaluation
- Regression testing for AI systems
- Tracing and observability
- Cost and latency management
- AI security and reliability
Tools you use
MLflowGitHub ActionsMonitoring ToolspgvectorHands-on lab
Build a regression test suite for an AI/RAG system to catch quality drops before release.
AI Solutions Engineering
Module 7 · 40 HrsReal applications touch client systems and private data. Use this module for API integration, security and PII handling, and the habit of documenting and demonstrating what you built.
See the full module →What you study
- Data and system integration
- API integration
- Security and PII handling
- Solution design and prototyping
- Cost estimation and technical documentation
- Architecture diagrams and client demonstrations
Tools you use
LLM APIsFastAPIREST APIsPostgreSQLHands-on lab
Prototype a client-ready AI solution with proper security and PII handling.
What the programme covers for this role. The programme teaches how to build applications on top of existing language models: prompts, retrieval, agents and evaluation. It does not include training a foundation model from scratch or a dedicated fine-tuning course, so those topics sit outside this syllabus.
Where a Generative AI Engineer course can take you
AI Application Developer or LLM Application Developer
The programme lists Generative AI Engineer, GenAI Application Engineer, LLM Application Developer and AI Application Developer as roles for this module. Entry positions often mean building one feature of a larger product.
Generative AI Engineer
With experience you own whole assistants or retrieval systems, including their evaluation and release, and you become the person a team asks about prompts, retrieval and answer quality.
Agentic and automation roles
A natural next step is Agentic AI Engineer, AI Agent Developer or AI Automation Engineer, building systems that use tools and follow multi-step workflows on a company's behalf.
LLMOps and solutions work
Some engineers move to LLMOps Engineer or AI Solutions Engineer roles. Placement assistance during the course includes resume, GitHub and LinkedIn help and mock interviews built around your RAG and agent projects.
Certifications the programme prepares you for
- NVIDIA Generative AI & LLMs
- Microsoft Azure AI Engineer
- IBM AI / Generative AI Engineering
- TensorFlow & Hugging Face Credentials
Generative AI Engineer course, quick answers
What does a generative AI engineer do?
A generative AI engineer builds applications powered by large language models, such as document question answering, assistants and text extraction. The work covers prompts, retrieval, an API layer, testing answers and keeping the system safe and affordable to run.
What is RAG and why does a generative AI engineer need it?
Retrieval-Augmented Generation finds relevant passages from your own documents and gives them to the model before it answers. It keeps replies tied to real sources and lets a system answer about data the model was never trained on.
Do I need machine learning knowledge before generative AI?
Not deeply. Generative AI work is mostly software engineering around an existing model. Basic ideas such as evaluation and embeddings help, and the course covers them, but you can start with Python and build up in order.
Will I train my own large language model in this course?
No. You learn to use existing models through APIs and libraries, and to build retrieval, agents and evaluation around them. Training or heavily fine-tuning a large model is a separate specialist area and is not part of this syllabus.
Which tools does a generative AI engineer use in Hyderabad companies?
Common tools include Python, FastAPI, LLM APIs, LangChain, LangGraph, PostgreSQL with pgvector, vector databases and Docker. The course covers each of these in labs, though every company chooses its own stack and you will keep learning on the job.
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Other roles in the AI / ML programme
Part of the Advanced AI & ML Certification Program
Every role course follows the same AI / ML programme, with the same modules, labs, projects and internship. See the full syllabus and every module.