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SOFTWARE & TECH LONG-TERM PROGRAM

Agentic AI, GenAI & LLM Application Development Course

Build and ship production LLM applications and agent systems.

DURATION
6 months
FEE
₹49,999
PAYMENT
₹10,000 × 6
FORMAT
Live · online
✓ Live mentor-led classes✓ Lifetime recordings✓ Certificate + placement support
Agentic AI, GenAI & LLM Application Development course training in India — Careers Ninza, Kolkata
QUICK ANSWERS

Agentic AI, GenAI & LLM Application Development course fee: ₹49,999 all inclusive, or No-Cost EMI of ₹10,000 × 6 with zero interest.

Duration: 6 months, part-time, with live weekday evening sessions from 7pm IST and weekend workshops.

Format: Live online across India, plus classroom batches in Kolkata, Asansol and Durgapur. Every session recorded and yours for life.

Who it suits: Beginner to job-ready. No prior experience required.

You finish with: A deployed LLM application, a verifiable certificate, and twelve months of placement support.

Roles it prepares you for: AI engineer, LLM application developer, GenAI engineer, Machine learning engineer.

About the Agentic AI, GenAI & LLM Application Development course

The engineering track: six months building real LLM applications — RAG systems, tool-using agents, evaluation harnesses and the deployment around them. Python-based, API-first, production-minded.

You ship working applications with tests, monitoring and cost controls, not prototypes that break on the second user.

Who this program is for

If two or more of these describe you, this is the right course.

— Developers moving into AI engineering
— Data and backend engineers adding LLM systems to their stack
— Technical founders building AI products
— Automation specialists graduating from no-code to code
— CS graduates targeting AI engineer roles

What you will learn

1 Build RAG pipelines: chunking, embeddings, retrieval, reranking
2 Design tool-using agents with planning and memory
3 Write evaluations that catch regressions before users do
4 Fine-tune and prompt-optimise where each actually pays off
5 Deploy with streaming, caching, rate limits and cost caps
6 Handle safety, prompt injection and data boundaries

Agentic AI, GenAI & LLM Application Development syllabus

12 modules, each taught live and reviewed by a mentor.

1 Module 1 — Engineering foundations for AI systems View
•Python for production: typing, async/await, dataclasses, dependency management with uv or Poetry
•Project structure, configuration management and twelve-factor principles for AI services
•Git workflow, code review and CI basics for an AI codebase
•HTTP and API design: REST conventions, status codes, idempotency, versioning
•Testing discipline: unit, integration and the specific difficulty of testing non-deterministic systems
•Logging, structured events and observability from the first commit
•Environment and secret management, and never committing an API key
2 Module 2 — Large language models: mechanics and selection View
•Transformer architecture: attention, positional encoding, decoder-only versus encoder-decoder
•Tokenisation, context windows, and the cost implications of both
•Sampling parameters: temperature, top-p, top-k, frequency and presence penalties
•Model landscape: OpenAI, Anthropic, Google, Mistral, Llama and open-weight deployment
•Capability versus cost versus latency, and routing tasks to the cheapest sufficient model
•Self-hosting open models: vLLM, quantisation, GPU sizing and when it beats API pricing
•Reasoning models and extended thinking: appropriate use and cost behaviour
•Multimodal inputs: images, documents, audio in production pipelines
3 Module 3 — Prompt engineering as software View
•System prompt design: role, constraints, output contract, refusal conditions
•Structured output with JSON schema and function calling, and validating every response
•Few-shot example selection and dynamic example retrieval
•Chain-of-thought, self-consistency and reflection patterns with their cost trade-offs
•Prompt templating, versioning and treating prompts as reviewable artefacts
•Prompt compression and token budget management at scale
•Handling long documents: map-reduce, refine and hierarchical summarisation patterns
•Systematic prompt improvement driven by evaluation results rather than intuition
4 Module 4 — Retrieval-augmented generation in production View
•RAG architecture end to end and where each stage typically fails
•Ingestion: parsers for PDF, DOCX, HTML, tables and scanned documents with OCR
•Chunking strategies: fixed, recursive, semantic, document-structure-aware, and parent-document retrieval
•Embedding models: selection, dimensionality, cost, multilingual support for Indian languages
•Vector databases: pgvector, Pinecone, Qdrant, Weaviate, Milvus — indexing, filtering, scaling
•Hybrid retrieval combining BM25 keyword search with dense vectors, and score fusion
•Reranking with cross-encoders and the measurable quality gain it produces
•Query transformation: rewriting, decomposition, HyDE, multi-query retrieval
•Citation, grounding verification and detecting when the model answered without support
•Evaluating retrieval independently: recall@k, MRR, NDCG before touching generation quality
•Incremental index updates, deletion and keeping a knowledge base current
•Graph RAG and structured retrieval where relationships matter
5 Module 5 — Agent architecture and tool use View
•Agent loop implementation: observe, plan, act, evaluate, repeat, with a stopping condition
•Tool definition: schemas, descriptions, error contracts, and how description wording changes behaviour
•Tool result handling, truncation and feeding large outputs back into limited context
•Planning patterns: ReAct, plan-and-execute, tree of thoughts, and their cost profiles
•Memory systems: conversation buffers, summarisation, entity memory, vector-backed long-term memory
•Multi-agent systems: supervisor-worker, sequential handoff, debate patterns and when they are over-engineering
•Frameworks compared: LangChain, LlamaIndex, LangGraph, CrewAI, and building without a framework
•State management and durable execution for long-running agents
•Idempotency, retries and preventing duplicate side effects
•Model Context Protocol and standardised tool interfaces
6 Module 6 — Evaluation, the discipline that separates shipping from demoing View
•Building a golden dataset: case selection, edge cases, labelling, maintenance
•Offline evaluation: exact match, semantic similarity, rubric scoring
•LLM-as-judge: prompt design for judges, bias mitigation, agreement with human raters
•Human evaluation workflow and inter-rater reliability
•Component-level evaluation: retrieval, generation, tool selection measured separately
•Regression testing in CI so a prompt change cannot silently degrade quality
•Production evaluation: sampling live traffic, feedback capture, drift detection
•Metrics that matter commercially: task success rate, escalation rate, cost per resolution
•A/B testing model and prompt changes against real users
7 Module 7 — Fine-tuning and model adaptation View
•When fine-tuning is justified versus prompting or RAG — the decision framework
•Dataset construction: size, quality, formatting, train-validation split
•Supervised fine-tuning, LoRA and QLoRA: mechanics and hardware requirements
•Hosted fine-tuning on OpenAI and other providers versus self-managed training
•Preference tuning concepts: RLHF and DPO in outline
•Distillation: training a small model on a large model’s outputs to cut cost
•Evaluating a fine-tuned model honestly against the base model baseline
•Deployment and versioning of fine-tuned models
8 Module 8 — Production engineering View
•FastAPI service design: endpoints, validation, dependency injection, background tasks
•Streaming responses with server-sent events and websockets
•Async concurrency, connection pooling and handling provider rate limits
•Caching: exact-match, semantic caching, prompt caching, and their invalidation problems
•Queueing and worker architecture for long-running agent tasks
•Fallback and failover across providers when an API degrades
•Cost control: token accounting per request, budget enforcement, model routing, alerting
•Latency optimisation: parallel tool calls, speculative execution, streaming first token
•Containerisation with Docker and deployment to cloud runtimes
•Load testing an LLM service and capacity planning
9 Module 9 — Safety, security and governance View
•Prompt injection: direct and indirect, with practical defences and input isolation
•Jailbreak resistance and output filtering
•Data exfiltration risks in tool-using agents and least-privilege tool design
•PII detection, redaction and data residency considerations under the DPDP Act
•Content moderation and refusal behaviour appropriate to the use case
•Audit logging sufficient for a regulated environment
•Model and vendor risk: lock-in, deprecation, pricing change, and abstraction layers
•Responsible deployment: disclosure, human oversight, and setting user expectations honestly
10 Module 10 — Observability and operations View
•LLM observability tooling: LangSmith, Langfuse, Phoenix and what to instrument
•Tracing a multi-step agent run end to end for debugging
•Dashboards: cost, latency, error rate, task success, escalation
•Alerting on quality regression rather than only on errors
•User feedback loops and turning thumbs-down into evaluation cases
•Incident response for AI systems and post-incident review
•Continuous improvement cadence: evaluation, hypothesis, change, measure
11 Module 11 — Applied builds View
•Customer-facing support assistant grounded in documentation with escalation to human
•Internal knowledge agent over company documents with role-based access control
•Document processing pipeline: extraction, validation, structured output, human approval
•Coding assistant integrated with a repository and test suite
•Data analysis agent that queries a warehouse and explains its findings
•Multi-agent research and report generation system
•Voice or WhatsApp-based assistant for Indian users with multilingual handling
12 Module 12 — Capstone: ship a production LLM application View
•Week 1–3: problem definition, architecture design, evaluation criteria agreed before building
•Week 4–8: core application built — retrieval, agent loop, tools, structured outputs
•Week 9–13: evaluation suite, regression tests in CI, and iteration driven by measured results
•Week 14–18: production hardening — streaming, caching, cost caps, fallbacks, guardrails
•Week 19–22: deployment, observability, load testing, monitoring dashboards
•Week 23–24: architecture review, handover documentation and defence before mentors
•Deliverables: deployed application, evaluation suite, monitoring dashboard, architecture decision records, GitHub repository

Tools you will work in

PythonFastAPILangChain / LlamaIndexOpenAI / Anthropic APIspgvector / PineconeDockerGit

What you walk out with

OUTCOME
A deployed LLM application
OUTCOME
An evaluation and monitoring suite
OUTCOME
A GitHub portfolio reviewers respect

Roles this prepares you for

Salary depends on your city, experience and the strength of your project, so we do not publish package figures we cannot substantiate.

AI engineerLLM application developerGenAI engineerMachine learning engineer

Fees and payment

TOTAL PROGRAM FEE
₹49,999
All inclusive. No registration or material charges.
PAYMENT PLAN
₹10,000 × 6
No interest, no processing fee. First instalment at enrolment.
✓  Live classes with a working practitioner
✓  Lifetime access to every recording
✓  Weekly doubt-clearing session
✓  Certified project reviewed by a mentor
✓  Rejoin any future batch free if you fall behind

Agentic AI, GenAI & LLM Application Development training across India

Batches run live online, so the class is identical wherever you join from — same trainer, same time, same fee. No city-based pricing. Classroom batches run in Kolkata, Asansol and Durgapur.

TIER 1 CITIES

Ahmedabad, Bengaluru, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, Pune.

TIER 2 CITIES

Agra, Ajmer, Amritsar, Bhopal, Bhubaneswar, Chandigarh, Coimbatore, Dehradun, Faridabad, Ghaziabad, Gwalior, Indore, Jaipur, Jamshedpur, Kanpur, Kochi, Lucknow, Mysore, Nagpur, Nashik, Patna, Raipur, Surat, Vadodara, Visakhapatnam.

TIER 3 CITIES

Bathinda, Bikaner, Cuttack, Etawah, Gandhinagar, Hajipur, Hosur, Jhansi, Junagadh, Madurai, Mathura, Meerut, Rajahmundry, Rohtak, Roorkee, Salem, Udaipur, Vijayawada.

See all locations across India →

Agentic AI, GenAI & LLM Application Development — frequently asked questions

Still unsure? Call an advisor on +91 90888 39993.

What is the Agentic AI, GenAI & LLM Application Development course fee in India?+

The Agentic AI, GenAI & LLM Application Development course fee at Careers Ninza is ₹49,999, inclusive of everything. You can pay it as ₹10,000 × 6 under our No-Cost EMI plan, with no interest and no processing charge. The fee covers all live classes, lifetime recordings, weekly doubt-clearing sessions, assessments, the mentor-reviewed capstone project and placement assistance.

Is Agentic AI, GenAI & LLM Application Development available online across India?+

Yes. Every batch runs live online, so you can join Agentic AI, GenAI & LLM Application Development from anywhere in India. We currently have students from Kolkata, Asansol, Durgapur, Siliguri, Howrah, Gwalior, Patna, Ranchi, Bhubaneswar, Guwahati, Pune, Mumbai and many other cities, plus learners outside India. On-site delivery is available in Kolkata, Asansol and Durgapur, and on request in other cities for corporate or campus groups.

How long is the Agentic AI, GenAI & LLM Application Development course and what is the weekly time commitment?+

Agentic AI, GenAI & LLM Application Development runs for 6 months. Plan on six to eight hours a week: two live weekday evening sessions plus project work, with occasional weekend workshops. Working professionals complete it without taking leave.

Do I need prior experience to join Agentic AI, GenAI & LLM Application Development?+

Beginner to job-ready. The course starts from first principles and the mentor calibrates pace to the batch. Basic comfort with a computer is enough; no prior coding background is assumed. If a specific prerequisite genuinely matters for your goal, the advisor will tell you honestly on the counselling call rather than take the enrolment.

Who teaches Agentic AI, GenAI & LLM Application Development?+

A working practitioner in the field, not a full-time trainer. Careers Ninza has more than 100 industry mentors who teach while still doing the job, so the examples come from current work rather than a textbook. You can ask which mentor is assigned to your batch before enrolling, or meet them at a free live masterclass.

What will I build during the Agentic AI, GenAI & LLM Application Development course?+

One real capstone project, scoped in the first fortnight and carried through every module: a deployed LLM application. It is reviewed by your mentor with written feedback, and it is what interviewers or clients actually discuss with you afterwards.

Is the Agentic AI, GenAI & LLM Application Development certificate recognised?+

You receive a verifiable Careers Ninza industry certificate with a unique ID and a public verification link, issued by Ninza Career Solutions Pvt. Ltd. once your project is accepted. It is an industry certificate, not a government-accredited degree or diploma, and we say so plainly. Its weight comes from the reviewed project behind it.

Does Agentic AI, GenAI & LLM Application Development include placement support?+

Yes. Long-term programs include portfolio review, resume and LinkedIn clinics, mock interviews with working professionals, and referrals to our hiring partner network, for twelve months after completion. There is no success fee and no income-share agreement. We do not guarantee employment, and we would be cautious of any institute that does.

What career roles does Agentic AI, GenAI & LLM Application Development prepare me for?+

AI engineer, LLM application developer, GenAI engineer, Machine learning engineer. Salary depends on your city, prior experience and the strength of your project, so we do not publish package figures we cannot substantiate; an advisor will give you an honest band for your target role and city.

What if I miss classes or fall behind in Agentic AI, GenAI & LLM Application Development?+

Every session is recorded and stays yours for life. There is a weekly doubt-clearing slot where you can bring your own work, and if you fall too far behind you may rejoin any future batch of Agentic AI, GenAI & LLM Application Development free of charge, with no conditions.

How is Agentic AI, GenAI & LLM Application Development at Careers Ninza different from a recorded online course?+

It is live and never pre-recorded. There is no cheaper self-paced tier, because removing the live class removes the reason the course works. Batches are capped so a quiet student still gets asked questions, and the mentor reviews your project personally rather than auto-marking a quiz.

Can I see a Agentic AI, GenAI & LLM Application Development class before paying?+

Yes. Careers Ninza runs free live masterclasses every week. Reserve a seat, watch a practitioner teach for ninety minutes, ask questions, and then decide. To enrol or to ask which batch suits you, call +91 90888 39993 or message us on WhatsApp.

How do I enrol in Agentic AI, GenAI & LLM Application Development?+

Call +91 90888 39993, email [email protected], or send an enquiry on WhatsApp from this page. An advisor confirms whether Agentic AI, GenAI & LLM Application Development fits your goal, then sends written confirmation of your batch, the fee and No-Cost EMI of ₹10,000 × 6. Seats are capped, so batches close once full.

Agentic AI, GenAI & LLM Application Development across India — city questions

Same class, same fee, same mentor, wherever you are.

Is Agentic AI, GenAI & LLM Application Development available in my city?+

Yes. Every batch runs live online, so learners across India attend the same class in real time — including Visakhapatnam, Guwahati, Patna, Ahmedabad, Surat, Vadodara, Rajkot, Faridabad, Gurugram, Bengaluru and every other city and town. There is no separate recorded version for students outside Kolkata, and no city has a different curriculum.

Does the Agentic AI, GenAI & LLM Application Development course fee change by city?+

No. The fee is ₹49,999 everywhere in India, with the same No-Cost EMI of ₹10,000 × 6. We do not price differently for metro and non-metro learners, and there are no travel or centre charges for online batches.

Do you have a classroom centre near me?+

Classroom batches run in Kolkata, Asansol and Durgapur. Everywhere else in India is served by live online delivery, which most working professionals and students prefer. On-site delivery in other cities is available for corporate teams and college groups on request.

What are the batch timings for people in different time zones within India?+

India runs on a single time zone, so timings are identical nationwide: weekday evening sessions from 7pm IST with weekend workshops. Every session is recorded and stays yours for life, so a late shift or travel never costs you a module.

Will placement support help me find work in my own city?+

Yes. Our hiring partner network covers roles across Indian cities, and a growing share of openings are remote and open to candidates anywhere in India. We share your profile only with your consent, for a specific role. We do not guarantee employment.

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Seats in every batch are capped

Talk to an advisor for fifteen minutes. We will tell you whether Agentic AI, GenAI & LLM Application Development fits your goal — or which program does instead.

APPLY NOW CALL +91 90888 39993