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Become an AI / GenAI Engineer
Ship production-grade LLM applications using RAG, agents, and fine-tuning on real infrastructure.
CREATED BY
K
Kiran N. [PLACEHOLDER] ★ 5.0
Site Reliability Engineer at NetSwitch | 5+ years of experience
About this Path
Built for senior engineers who already write Python and understand ML basics but need to productionize generative AI. You will move from prompt tinkering to architecting multi-agent systems, retrieval-augmented pipelines, and fine-tuned models behind real APIs. By the end you will have shipped at least one end-to-end GenAI service you can demo in interviews.
Path Overview
Advanced LevelCertificate of CompletionAbout 72 hours to completeEnglish language22+ curated videosLearn online at your own pace6 modules with resourcesGamified & interactive
Path Curriculum
Transformer internals and tokenization
Understand attention, KV cache, and why context length matters for cost.
Advanced prompting patterns
Chain-of-thought, few-shot, ReAct, and structured output with JSON mode.
Model selection trade-offs
Compare GPT-4o, Claude 3, Gemini 1.5, and open-source alternatives on latency and cost.
Evaluation baselines
Set RAGAS and LLM-as-judge benchmarks before writing a single line of app code.
Chunking and embedding strategies
Semantic vs. fixed-size chunking, dense embeddings, and hybrid BM25 + vector search.
Vector store operations at scale
Index, query, and filter with Pinecone, Weaviate, and pgvector under real load.
Advanced RAG patterns
HyDE, multi-query retrieval, re-ranking with Cohere, and contextual compression.
RAG evaluation and iteration
Measure faithfulness, context precision, and answer relevance with RAGAS dashboards.
Dataset curation and formatting
Build instruction-tuning and DPO datasets from real production logs and annotations.
QLoRA fine-tuning with Hugging Face
Run LoRA adapters on Llama 3 and Mistral on a single A100 via Unsloth.
Serving fine-tuned models
Deploy adapters with vLLM, quantize with GPTQ/AWQ, and benchmark tokens-per-second.
OpenAI and Bedrock fine-tune APIs
Use managed fine-tuning for GPT-4o mini and evaluate against the base model.
Tool use and function calling
Design reliable tool schemas, handle errors, and chain tool calls without infinite loops.
LangGraph stateful agents
Build checkpointed, branching agent graphs with human-in-the-loop approval nodes.
Multi-agent orchestration with AutoGen
Coordinate specialized agents for research, coding, and review in a single workflow.
Memory architectures
Implement short-term, long-term, and episodic memory using Redis and vector stores.
Tracing with LangSmith and Arize Phoenix
Capture every span, token count, and latency across chained LLM calls in production.
Hallucination detection pipelines
Build automated grounding checks using NLI models and factual consistency scorers.
Content safety and guardrails
Integrate Llama Guard and AWS Bedrock guardrails to block harmful outputs at runtime.
CI/CD evaluation gates
Block deploys when RAGAS or LLM-judge scores regress beyond a defined threshold.
Streaming APIs and structured output
Build sub-200ms streaming endpoints using FastAPI, SSE, and Instructor for typed responses.
Deploying on AWS Bedrock and Vertex AI
Provision managed inference endpoints, set token budgets, and configure auto-scaling.
Cost optimization strategies
Use prompt caching, batching, model routing, and smaller specialized models to cut spend.
GenAI system design interview prep
Walk through real interview prompts: design a RAG-powered code review assistant end-to-end.
What you'll learn
- ✓Design and evaluate RAG pipelines using LangChain, LlamaIndex, and vector stores such as Pinecone and pgvector.
- ✓Fine-tune open-source LLMs (Llama 3, Mistral) with QLoRA on custom datasets and serve them via vLLM.
- ✓Build reliable multi-agent systems using LangGraph and AutoGen with tool use, memory, and human-in-the-loop checkpoints.
- ✓Instrument LLM applications with LangSmith and Arize Phoenix to catch hallucinations and latency regressions.
- ✓Deploy GenAI services on AWS Bedrock and GCP Vertex AI with cost controls and content safety guardrails.
- ✓Architect end-to-end AI products with streaming APIs, structured output (Instructor/Pydantic), and CI/CD evaluation gates.