Architect Enterprise GenAI Platforms. The Best Generative AI Engineering Course in Ahmedabad.
Enterprises are shipping LLM copilots, not chatbot demos — they need engineers who design RAG pipelines, fine-tune with LoRA, enforce guardrails, and deploy streaming APIs under cost controls. We train you on LangChain, vector stores, Hugging Face, and production LLMOps patterns used by banks, IT services, and product teams building GenAI features across India.
Enterprise Enrollment Architecture
Duration: 50 Hours
₹55,000₹40,000
Note: Any type of tax would be extra. Global certification cost is excluded.
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Bascom Bridge Alumni Engineering Global Tech
Exclusive Program Benefits
- After the course, Bascom Bridge will share 10–12 sample CVs to help build your resume.
- Students receive a license for Bascom Bridge’s placement mobile app*.
- Lifetime access* to the enrolled course for students.
- If a student does not clear interviews, Bascom Bridge will provide retraining* until employment is secured.
- Global certification training is included at no extra cost.
- Discount on global certification fees* available.
- Hands-on RAG, agent, and fine-tuning labs with governed API usage and cost-tracking dashboards.
- Portfolio capstone: enterprise knowledge copilot with citations and guardrails — GenAI interview-ready*.
Applicable taxes will be added to each instalment.
Trusted by Government of India & Leading PSUs
About Generative AI Engineering
Enterprise Tools Included
- LangChain & LangGraph — RAG chains, agents, and tool routing
- OpenAI / Azure OpenAI & open-weight models (Llama, Mistral)
- Hugging Face Transformers — PEFT, LoRA, and supervised fine-tuning
- ChromaDB / FAISS — embeddings, hybrid search, and chunking strategies
- FastAPI + Docker — streaming LLM APIs with auth and rate limits
Cognitive Prerequisites
- Applied Data Science, Enterprise ML, or strong Python + API fundamentals
- Comfort with JSON, REST APIs, and basic prompt/response patterns
- Understanding of embeddings and retrieval concepts (introduced in Module 1)
- Laptop with 16 GB RAM; cloud LLM API credits provided for lab exercises
Salary Progression (₹)
- Entry-Level (0-3 yrs)₹8.0L - ₹14.0L
- Mid-Level (4-7 yrs)₹18.0L - ₹35.0L
- Senior Level (8-12+ yrs)₹35.0L - ₹60.0L+
The Enterprise Capstone Architecture
Regulated Enterprise Knowledge Copilot. You will ingest PDF and policy corpora with metadata-aware chunking, build a hybrid RAG pipeline in LangChain with re-ranking and inline citations, add PII guardrails and prompt-injection filters, optionally fine-tune a LoRA adapter for domain tone, and deploy a streaming FastAPI service in Docker with token-cost dashboards and evaluation reports — the deliverable pattern BFSI and IT services GenAI teams present before production sign-off.
Deep-Dive Syllabus Grid
Module 1: LLM Landscape & Transformer Foundations
How autoregressive transformers work, tokenization, context windows, and model families (GPT-class, Llama, Mistral). Mapping enterprise use cases — copilots, summarization, codegen — to the hiring bar for GenAI engineers at banks, IT services, and Ahmedabad product startups in 2026.
Module 2: Prompt Engineering & Structured Outputs
System/user/assistant roles, few-shot templates, chain-of-thought guardrails, JSON mode, and Pydantic-validated outputs. Building evaluation harnesses with golden questions — the baseline skill before any RAG or fine-tuning investment.
Module 3: Embeddings & Vector Database Architecture
Chunking strategies, overlap, metadata filters, cosine similarity, and hybrid keyword + dense retrieval. Implementing ChromaDB/FAISS indexes on policy manuals, HR handbooks, and support tickets — patterns GCCs use for internal knowledge search.
Module 4: Retrieval-Augmented Generation (RAG) Pipelines
Ingestion DAGs, re-ranking, citation formatting, and hallucination reduction. End-to-end RAG with source attribution — what legal and compliance teams require before approving customer-facing copilots in India.
Module 5: LangChain & Orchestration Patterns
LCEL chains, retrievers, memory buffers, and RunnableParallel flows. Composing multi-step pipelines without spaghetti notebooks — maintainable GenAI code that passes senior engineer review.
Module 6: Fine-Tuning with PEFT / LoRA
When RAG is not enough: domain adaptation, instruction datasets, LoRA rank selection, and evaluation on held-out enterprise Q&A. Cost-aware training on open-weight models versus API-only approaches for regulated industries.
Module 7: Agentic Workflows & Tool Use
ReAct-style agents, function calling, SQL/API tools, and human-in-the-loop approvals. Building invoice parsers and ticket triage agents with bounded autonomy — avoiding runaway tool loops in production.
Module 8: Guardrails, Safety & Red-Teaming
PII detection, prompt-injection defenses, output filters, refusal policies, and audit logs. Aligning with DPDP expectations and enterprise risk committees before deploying GenAI to customer data.
Module 9: Cost, Latency & Caching Optimization
Token budgeting, streaming responses, semantic caches, batching, and model routing (small vs large). Operating LLM features within Gujarat enterprise IT budgets without surprise cloud bills.
Module 10: Production LLM APIs with FastAPI & Docker
SSE streaming endpoints, API keys, rate limiting, health checks, and containerized deploys. Shipping a copilot service the way platform teams integrate with SAP, CRM, and mobile apps.
Module 11: Observability & LLMOps Basics
Tracing prompts/responses, latency SLAs, feedback loops, and A/B prompt versions. LangSmith-style debugging concepts and regression suites so model updates do not break live workflows.
Module 12: Capstone Review & GenAI Interview Drills
System-design scenarios: multi-tenant RAG, rollback, evaluation metrics, and failure modes. Presenting architecture diagrams, cost estimates, and demo videos — the interview standard for LLM engineer roles in India.
Top private enterprises we train across India
