Core responsibilities this discipline prepares you for.
1
Design and build production GenAI features
(chatbots, search, summarization) into web applications
- Build streaming chat UIs with FastAPI backends using SSE and WebSocket transports
- Wire React frontends to LLM-powered APIs with end-to-end full-stack integration
- Deploy complete GenAI applications from prototype to production on Kubernetes
2
Implement RAG pipelines
with vector databases for enterprise search and knowledge retrieval
- Build end-to-end RAG: document chunking → embedding generation → pgvector storage → LangGraph retrieval nodes
- Validate retrieval accuracy using RAGAS metrics and implement self-verification loops
- Benchmark chunking strategies and HNSW/IVFFlat index types against precision-recall tradeoffs
3
Optimize LLM inference
for latency, cost, and reliability across multiple providers
- Configure multi-provider routing with LiteLLM gateway including load balancing and failover
- Implement semantic caching with Redis + embedding similarity to reduce costs by 40%+
- Extract structured outputs with Pydantic AI and handle provider-specific error recovery
4
Integrate LLM APIs
(OpenAI, Gemini, Anthropic) into existing applications with error handling
- Connect to OpenAI, Anthropic, and Gemini APIs with streaming, function calling, and embeddings
- Build FastAPI rate limiting middleware with exponential backoff and retry logic
- Navigate provider contract differences across authentication, token limits, and response formats
5
Build GenAI agent features
with tool calling, function execution, and human-in-the-loop workflows
- Design LangGraph state machines with structured tool calling and JSON schema validation
- Implement MCP tool integration for dynamic tool discovery and execution
- Wire interruptible agent workflows with human approval gates and checkpoint persistence
6
Evaluate model outputs
using automated metrics and LLM-as-judge for production quality
- Build evaluation pipelines using RAGAS faithfulness/relevance metrics and DeepEval harnesses
- Integrate LLM-as-judge scoring into CI/CD gates for automated quality control
- Track quality metrics over time with Langfuse dashboards and regression detection
7
Deploy and containerize
GenAI applications on Kubernetes with CI/CD
- Containerize FastAPI + LLM applications with multi-stage Docker builds
- Deploy to Kubernetes with Helm charts, readiness probes, and Ingress configuration
- Automate rollouts with ArgoCD GitOps workflows and Kustomize environment overlays