Gen AI & Agentic AI
- 300+ Hours of training
- 450+ Hiring Partners
- Classroom & Live Online Training Available
- Hands-on training with assignments and capstone projects
- Next batch starts 10 Nov 2026 · Cohort Kailash
DedicatedPlacement Assistance
About the Gen AI & Agentic AI course
Go from foundational large language model concepts to building agentic systems that plan, use tools, and act autonomously with minimal supervision. The curriculum covers prompt engineering and evaluation, retrieval-augmented generation for grounding models in real data, and the design patterns behind orchestrating multi-step agent workflows across tools and APIs. You'll build progressively more capable projects — from a single well-tuned prompt to a working multi-agent system — while learning how to reason about reliability, cost, and safety trade-offs that come up when putting generative AI into production.
Learn From The Real Forward Deployed Engineers And AI Experts
Guest lectures and mentorship from Forward Deployed Engineers working at leading companies.
Companies Hiring In This Field
Companies that recruit for roles in this field.
Amazon
IBMWho Should Do The Gen AI & Agentic AI?
Who this program is built for.
- Working professionals looking to upskill or switch domains01
- M.Tech / M.E. graduates looking to specialize or pivot into a new tech track02
- B.Tech / B.E. graduates or final-year students in CS, IT, ECE, or related branches03
- BCA, MCA, or B.Sc / M.Sc (Computer Science, IT, or related) graduates04
- 05M.Com, B.Com, MBA, or BBA graduates
- 06Diploma holders in Computer Science, IT, or Engineering
- 07No prior coding experience required for beginner-track courses
- 08Final-year students and recent graduates from any stream
Gen AI & Agentic AI Learning Path
Learn → Build → Deploy → Solve Real Problems → Get Placed
Tech Stack
Technologies & Tools
Placement Path
From your first class to your first offer letter, here is how we take you there.
Career Opportunities with Gen AI & Agentic AI
Tap or hover a card to see what the role looks like day to day. Pay ranges are indicative market figures, not a guarantee of salary or placement.
Prompt EngineerIndicative market pay: ₹6 – 10 LPA
Prompt Engineer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
AI Agent DeveloperIndicative market pay: ₹8 – 13 LPA
AI Agent Developer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
LLM Application EngineerIndicative market pay: ₹9 – 15 LPA
LLM Application Engineer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
RAG Systems EngineerIndicative market pay: ₹10 – 16 LPA
RAG Systems Engineer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
GenAI EngineerIndicative market pay: ₹10 – 16 LPA
GenAI Engineer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
AI Solutions EngineerIndicative market pay: ₹12 – 18 LPA
AI Solutions Engineer
A role you can aim for after Gen AI & Agentic AI. We help you prepare with projects, mock interviews and resume reviews.
Everything That You Get by Enrolling in Gen AI & Agentic AI
We've built this program with one goal — to guide, support, and uplift you at every stage of your career journey. From the first class to your first placement and beyond, every component has been created with your growth, confidence, and success in mind.
EMI Options Available
Placement Assistance
Collaborative Learning through Teamwork
Course Completion Certificate
Expert Career Guidance
Flexible Learning
ISO 9001:2015 Certified Institute
Hands on Project
Corporate-Ready Curriculum
Industry Expert Sessions
Internship programs
Mock Interview
Placement Mentorship
Resume Preparation Guidance
Recognised worldwide
ISO
IAF
MSME
DPIIT
Accreditation and Recognition from Top Industry Bodies
Our programs and certifications are affiliated with, and recognised by, globally respected standards and skill-development bodies.
Gen AI & Agentic AI Curriculum
Comprehensive modules designed to build Gen AI & Agentic AI skills step by step.
Build the Python foundation needed for GenAI and agentic development.
Key Topics
- Dev environment: Jupyter, VS Code, PyCharm, Anaconda, Google Colab
- Data types, operators, strings, lists, tuples, sets & dictionaries
- Control flow, match-case, loops & comprehensions
- Functions, *args/**kwargs, lambda, modules & packages
- Iterators, generators & decorators
- File handling, logging, error & exception handling
- OOP: classes, inheritance, access modifiers, polymorphism, abstraction
- Asynchronous programming: async/await, asyncio, gather/create_task, httpx & aiohttp
- Pydantic: models, validators, BaseSettings, nested models, use with FastAPI & LangChain
Industry Relevance
Automate repetitive analysis tasksWrite clean, reusable codeDebug and log real programsUse AI coding assistants responsiblyUnderstand how LLMs work before building on top of them.
Key Topics
- GenAI vs traditional AI, real-world products, no-code tools (Bolt, Lovable, V0, n8n)
- Transformer architecture, self-attention, encoder vs decoder, positional encoding
- Tokens, context windows, streaming, function calling, rate limiting & token optimization
- LLM APIs: OpenAI, Claude (Messages API), Gemini; authentication, error handling, provider fallback
- Temperature, top-p, top-k; system vs user prompts; multi-turn context management
- Hallucination causes & mitigation; fine-tuning vs prompting vs RAG
Industry Relevance
Choose the right model per taskCall LLM APIs reliablyControl cost and latencyReduce hallucinationsMaster prompt patterns that produce reliable, high-quality output.
Key Topics
- Prompt architecture, role prompting, zero-shot & few-shot
- Chain-of-thought, tree-of-thought, ReAct, self-consistency, meta-prompting, prompt chaining
- Prompt injection & defense patterns
- Prompt optimization, evaluation & scoring, A/B testing prompts, DSPy basics, versioning
- Structured output: JSON mode, function calling, Pydantic parsing, retries on malformed output
Industry Relevance
Write production promptsEvaluate prompts systematicallyGet structured JSON from LLMsDefend against prompt injectionUnderstand how meaning is represented and searched at scale.
Key Topics
- Embeddings: OpenAI models, sentence-transformers, cosine/dot/L2 similarity
- Chunking: fixed, recursive, semantic, document-aware, overlap & metadata
- Vector DBs: FAISS, ChromaDB, Pinecone, Qdrant, Weaviate
- Indexing: HNSW, IVF, ANN search, metadata filtering, batch upserts
- Advanced search: BM25, hybrid retrieval, HyDE, RRF, cross-encoder reranking, multi-vector
- Production pipelines: batch embedding, caching, incremental updates, quality monitoring, cost optimization
Industry Relevance
Build semantic searchPick the right vector databaseTune retrieval qualityRun embedding pipelines at scaleConnect LLMs to external knowledge reliably.
Key Topics
- RAG architecture: index, retrieve, generate; naive vs advanced RAG
- Advanced techniques: hybrid retrieval, memory-aware & multi-document RAG, contextual compression
- Evaluation: RAGAS, hallucination reduction
- LangChain RAG: loaders, splitters, retrievers, ConversationalRetrievalChain, LCEL
- LlamaIndex: index types, query & chat engines, sub-question engine, workflows
- Fine-tuning & local models: PEFT, LoRA, QLoRA, DPO, GGUF, Ollama
- Mini projects: document Q&A bot, knowledge-base assistant, multi-document research assistant
Industry Relevance
Ground answers in company dataEvaluate RAG with RAGASRun local open modelsProductionize retrieval pipelinesBuild robust AI pipelines with the most widely used LLM framework.
Key Topics
- LangChain core: LCEL, chains, prompt templates, output parsers, runnables
- Tools: built-in & custom (@tool), schemas, error handling & fallbacks
- Memory: buffer, summary, entity, vector-store memory, LangMem
- Retrievers & agents: ReAct, OpenAI Functions agent, executors & callbacks
- LangSmith: tracing, debugging, evaluation datasets, monitoring
- LangServe: serving as APIs, streaming, Docker, auth & rate limiting, deploy to Render/Railway/EC2
Industry Relevance
Compose multi-step LLM chainsTrace and debug with LangSmithServe chains as APIsAdd memory to assistantsUnderstand what makes a system truly agentic before building one.
Key Topics
- Agents vs LLM apps; tools, memory, planning, reasoning; agent loop
- Architectures: ReAct, plan-and-execute, Reflexion, Toolformer, supervisor/worker
- Workflow control: branching, retries, fallbacks, self-evaluation, human-in-the-loop
- Safety: input validation, output filtering, policy enforcement, prompt-injection defense
- Cost & token budget guardrails, observability & audit trails
Industry Relevance
Choose the right agent architectureAdd human approval stepsPut guardrails on autonomyAudit agent behaviourBuild production-grade stateful and cyclic agent workflows.
Key Topics
- LangGraph vs LangChain; StateGraph, nodes, edges, state schema
- State-based systems: TypedDict state, cycles, parallel fan-out/fan-in
- Checkpoints & persistence (SQLite, Redis), time-travel debugging, streaming
- Advanced patterns: interrupt & resume, dynamic routing, sub-graphs, LangGraph Cloud deployment
- Mini project: multi-step autonomous research workflow
Industry Relevance
Build resumable agent workflowsDebug agents with checkpointsCompose sub-graphsDeploy stateful agentsDesign and orchestrate teams of specialized AI agents.
Key Topics
- Design patterns: role-based agents, coordinator/worker, shared state, message passing
- CrewAI: crews, agents, tasks, roles (researcher, writer, planner, critic), flows
- Microsoft AutoGen: conversations, group chat, human proxy, memory, code execution
- Mini projects: CrewAI researcher + writer + critic; AutoGen automated report generation
Industry Relevance
Automate multi-step business workflowsSplit work across specialist agentsCoordinate agent communicationGenerate reports with agent teamsIntegrate AI into real developer workflows with the Model Context Protocol.
Key Topics
- MCP architecture: servers, clients, custom Python servers, tool schemas, resources, prompts, sampling
- Tool integration: file system, databases, APIs; MCP with LangChain & LlamaIndex; security
- AI coding workflows: Cursor, Cline, GitHub Copilot, rules & context, AI-assisted review
- OpenAI Responses API, function-calling patterns, AI-powered testing & documentation
- Mini project: MCP-enabled AI coding assistant
Industry Relevance
Expose enterprise tools via MCPSpeed up delivery with AI IDEsSecure MCP integrationsConnect databases to agentsOperate AI systems reliably in production.
Key Topics
- CI/CD with GitHub Actions: lint, test, build, push; Docker workflows; staging vs production
- Automated testing for AI: prompt tests, integration tests
- Observability: LangSmith, Weights & Biases, Prometheus & Grafana, structured logs, OpenTelemetry, Sentry
- Scaling: horizontal scaling, load balancing, queues (Celery, Bull, SQS)
- Reliability: retries, circuit breakers, graceful degradation, SLA & uptime monitoring
- Mini project: production deployment pipeline (GitHub Actions + Docker + Prometheus + LangSmith)
Industry Relevance
Automate AI deploymentsMonitor quality and uptimeScale for traffic spikesRecover gracefully from failuresDesign, build, deploy and present a complete agentic AI application.
Key Topics
- Problem definition, data sources and success metrics
- RAG + multi-agent system build with LangGraph / CrewAI
- Evaluation: golden dataset, RAGAS metrics, guardrails
- Deployment with Docker, CI/CD and monitoring (LangSmith)
- Portfolio curation: GitHub, LinkedIn & demo video
- Demo day & mock interviews
Industry Relevance
Own an agentic project end to endMeasure and prove answer qualityDeploy and monitor in productionBuild a recruiter-ready portfolio
The L&L AI Solutions Placement Mentorship Program
At L&L AI Solutions, we treat every learner as a professional in progress. Our Placement Mentorship Program pairs you with dedicated mentors who guide, support, and push you through every stage of your journey — from your first module to your final interview — so you leave not just trained, but genuinely ready to build a career in Gen AI & Agentic AI.
Stage 01
Sharpen Your Fundamentals & Interview Confidence
You'll work closely with mentors who pressure-test what you know and flag exactly where to focus next. Through regular check-ins, simulated interviews, and guided practice, you'll build the technical clarity and communication confidence to hold your own in any interview — technical or HR.
Skill Gap Assessments
Confidence Coaching
Technical Interview Simulations
Weekly Progress Scorecards
HR Round Simulations
Concept Refresher Sessions
Communication Skill Coaching
Key Support
Stage 02
Map Your Career Path & Build Your Professional Profile
Get complete clarity on how placements actually work — the roles open to you, what employers expect, and where you're the strongest fit. With a mentor by your side, you'll rebuild your resume and LinkedIn profile around the roles you're targeting, so your profile reads as capable as you actually are.
Career Path Walkthroughs
What to Expect from Placements
Resume Rebuild Support
LinkedIn Profile Makeover
Role Eligibility Reviews
Personal Branding Guidance
Extra Upskilling Support
Key Support
Stage 03
Apply, Interview & Land the Role
This is where preparation turns into real opportunity. Our placement team matches your profile to live openings, preps you for each specific interview, coordinates directly with employers, and stays with you — feedback and all — until an offer is in hand.
Live Job Opportunities
Profile-to-Role Matching
Role Briefings
Application Support
1:1 Interview Prep
Interview Scheduling Help
Post-Offer Support
Key Support
Gen AI & Agentic AI — FAQs
Everything you need to know about the course and admissions.
Classes are held live online and at our campus: 5th Floor, Draper Startup House Building, Rajiv Gandhi Nagar, Gachibowli, Hyderabad 500032.
Build with LLMs, prompt engineering, and autonomous agent systems.
Eligibility depends on your background. See the "Who Should Do The Gen AI & Agentic AI?" section on this page for the details.
The program runs for 6 Months.
The next cohort starts on 10 Nov 2026. Contact our admissions team to confirm seats and timings.
Yes. You receive a certificate of completion once you finish the program requirements.
Yes. We provide placement assistance, including mentorship, resume support and mock interviews. Placement assistance is not a guarantee of a job.
Yes. The course is built around hands-on practice and projects, ending with a capstone project you can add to your portfolio.
Yes. Mentors review your work and clear doubts throughout the program, not only during the capstone.
A laptop with a stable internet connection. Setup for the tools used is covered at the start of the course.
Our refund and cancellation terms are published on the Refund Policy page of this website. Please read them before you enrol, or ask our admissions team.
