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Work\n\nResponsibilities\nBuild Agentic Systems: Design supervisor/executor patterns, memory strategies, and robust tool-calling failure handling.\n\nLLM Adaptation & Deployment: Fine-tune open-source models and optimize inference for production-scale latency and cost.\n\nAdvanced RAG: Implement high-performance embedding, retrieval, and re-ranking pipelines for grounded outputs.\n\nStructured Generation: Enforce schemas and guardrails to minimize hallucinations and ensure reliable system behavior.\n\nEvaluation & Quality: Develop automated evaluation harnesses, regression tests, and versioning for prompts and models.\n\nProduction Engineering: Ship containerized APIs with full CI/CD, observability, and reliability monitoring (SLOs).\n\nCross-functional Delivery: Collaborate with product teams to integrate GenAI features and mentor junior engineers.\n\nRequirements\nSenior AI Expertise: 5+ years building production ML/AI systems, including 2+ years in lead roles with strong Python engineering (performance, testing, packaging).\n\nLLM & Agentic AI: Hands-on experience with orchestration, tool-calling, and workflow integration, including LLM adaptation (PEFT/LoRA) and safety engineering.\n\nProduction RAG & Data: Proven track record of operating RAG pipelines, vector databases, and retrieval performance tuning in production.\n\nMLOps & Cloud: Proficiency in containerized services (REST/gRPC), CI/CD, and monitoring within cloud environments (AWS/GCP/Azure).\n\nAdvanced Optimization: Experience in inference optimization (vLLM/quantization), event-driven orchestration, and automated evaluation 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