Principal AI Engineer

• 8–14 years of software engineering experience, with strong hands-on large-scale Python
• Working depth in at least one systems or backend language — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it
• Strong data structures and algorithms. 
• Strong understanding of APIs, microservices, and system design
• Hands-on experience building and operating data pipelines and production-grade distributed systems.

Agentic AI and LLMs

• 2+ years of hands-on LLM engineering, with at least couple agentic system you designed and took to production
• Production experience with agent frameworks — LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent — and the fluency to move between them as the ecosystem evolves
• Experience building MCP (Model Context Protocol) servers and tool-calling interfaces
• RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
• Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents)
• Design of guardrails and reliability patterns — validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths

Optimization

• Deep familiarity with token optimization and context-window management — context shaping, pruning, and compaction
• Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls
• Performance testing and tuning systems against defined SLOs