senior AI Engineer

Core Language & Architecture Python 3.11+ Advanced type hints (PEP 484), static typing discipline Async programming (asyncio, async/await, async generators) aiohttp / httpx (async HTTP clients) Pydantic v2 (BaseModel, validation, settings management) Structured logging & tracing patterns Redis (pub/sub, TTL, async clients) REST API design & integration patterns Retry/backoff strategies (Tenacity) Concurrency patterns (parallel tool calls, task orchestration) AI / LLM / Agent Systems LangGraph (state machines, conditional edges, checkpointing) LangChain 0.3.x (LLMChain, StructuredTool, retrievers, prompt templates) ReAct-style agent architectures Tool-based agent design (40+ tool environments) Azure OpenAI / OpenAI APIs (GPT-4o, deployment mgmt, rate limits, token budgeting) Prompt engineering (few-shot, structured output, JSON mode) PydanticOutputParser / structured LLM responses Guardrails / PII redaction patterns Memory abstractions for agents Langfuse (trace instrumentation, evaluation, prompt management) LLM fallback chains & error recovery RAG prompt grounding strategies LLM fine-tuning Neural Network training & tuning Traditional ML models (random forest, k-means clustering, linear regression, etc.) MCP development and consumption Retrieval, Search & RAG Engineering Vector databases (Qdrant and/or Milvus) HNSW indexing parameters Filtering strategies Embedding pipelines (OpenAI ada-002 or equivalent) Batch embedding & re-indexing workflows Hybrid retrieval (BM25 + semantic) Score fusion strategies Cross-encoder reranking (BAAI/bge models) FastAPI-based inference services LangChain retriever abstractions RAG evaluation metrics: Faithfulness Relevance NDCG MRR Trace-level RAG evaluation (Langfuse) Show more Show less