Python AI Engineer (Prompt & Agentic Systems)

Job Summary for Python AI Engineer (Prompt & Agentic Systems): - Develop end-to-end AI-enabled applications using Python, focusing on Large Language Models (LLMs) and agentic (autonomous/multi-agent) system architectures. - Design and build Python-based backend services that leverage LLMs for reasoning, extraction, summarization, and decision support. - Engineer, version, and evaluate AI prompts/system instructions; create guardrails and optimize for reliability, latency, and cost. - Architect and implement agentic systems, including planning, tool usage, memory, error recovery, and human-in-the-loop controls. - Build retrieval-augmented generation (RAG) pipelines: ingest documents, chunk data, generate embeddings, and implement semantic/vector search. - Define and monitor metrics for quality, safety, and performance of AI systems; establish observability for LLM calls. - Integrate APIs, databases, and workflow tools to enable agent capabilities, handling authentication, rate limits, and fallbacks. - Package and deploy services using Docker/Kubernetes; manage secrets, CI/CD pipelines, and support cost governance. - Ensure security and compliance, including PII handling, data privacy, prompt injection defense, and audit logging. - Collaborate cross-functionally with product, data, and security teams to deliver reliable and innovative AI features. Let me know if you want a more condensed/shortened version!