Lead Principal Agentic RAG Engineer - Python & AI Platforms

About the Role\nWe are hiring a senior, hands-on Agentic RAG Engineer to design, build, and operate the Retrieval-Augmented Generation platforms that power our autonomous AI agents.\nThis is a lead-by-example role :\nYou design the architecture\nYou write the Python\nYou ship to production\nYou mentor engineers by building real systems\nYou will lead the technical direction of RAG and agent memory systems , while remaining deeply involved in implementation, observability, and operational readiness.\nGCP is our primary platform, but all designs should be multi-cloud capable .\n\nKey Responsibilities\nRAG & Backend Engineering (Python-First)\nDesign and build production-grade RAG pipelines\nImplement:\nRetrieval strategies\nVector database integrations\nAgent memory and state management\nPrompt orchestration and chaining\nBuild scalable Python services using FastAPI / Django / similar\nIntegrate LLM APIs (OpenAI, Claude, Gemini) and open-source models (Llama, Mistral)\nImplement model/version rollout, rollback, and simulation testing\n\nAgentic Systems & Workflow Design\nBuild and operate multi-step agent workflows\nEnable:\nTool calling\nHuman-in-the-loop interventions\nSafe agent execution patterns\nDefine patterns for:\nPrompt versioning\nContext management\nToken and cost control\nCollaborate closely with AgentOps to ensure production-safe execution\n\nFull-Stack & Observability\nDesign and contribute to internal UIs for:\nAgent execution monitoring\nDecision and reasoning audits\nPrompt testing and visualization\nImplement structured logging and telemetry for:\nRetrieval quality\nAgent decisions\nToken usage and latency\nWork with Prometheus, Grafana, OpenTelemetry, or ELK-based stacks\n\nCloud, DevOps & Production Readiness\nOwn deployment pipelines for RAG and agent services\nWork hands-on with:\nDocker\nKubernetes\nTerraform\nCI/CD pipelines\nEnsure secure API design, auth, sandboxing, and operational guardrails\nOptimise for scalability, performance, and cloud cost efficiency on GCP\n\nTechnical Leadership & Team Enablement\nAct as technical lead for Agentic RAG engineering\nSet architectural standards and best practices\nReview code and designs with a high bar\nMentor engineers in:\nPythonic system design\nRAG correctness and evaluation\nProduction-grade GenAI systems\nPartner with Product and Platform leads on roadmap and delivery\n\nRequired Skills & Experience\nCore Engineering\n5+ years of strong Python engineering experience\nProven backend or full-stack development background\nExperience with FastAPI, Django, Flask, or similar frameworks\nComfort contributing to frontend systems (React / Next.js / Vue) when needed\n\nRAG, LLMs & Agentic Systems\nHands-on experience building RAG pipelines in production\nStrong understanding of:\nVector databases and retrieval strategies\nPrompt chaining and context handling\nAgent workflows and tool invocation\nExperience with frameworks such as:\nLangChain\nLangGraph\nLlamaIndex\nAutoGen / CrewAI\n\nCloud & Platform Engineering\nStrong experience with GCP , including:\nVertex AI\nGKE / Compute Engine\nCloud Functions\nCloud Storage, Pub/Sub\nHands-on DevOps skills:\nDocker\nKubernetes\nTerraform\nCI/CD tooling\nUnderstanding of secure APIs, auth, and sandboxing patterns\n\nNice to Have\nMulti-cloud experience (AWS, Azure)\nExperience with Responsible AI, guardrails, and eval frameworks\nContributions to open-source AI or infrastructure projects\nExperience building internal tooling or monitoring dashboards for AI systems\n\nWhat Success Looks Like\nRAG systems are accurate, observable, and cost-efficient\nAgent failures are explainable and debuggable\nEngineers follow clear, scalable RAG patterns you defined\nProduct teams trust agent outputs in production\nYou are the technical authority for RAG at BridgeAI\n\nWhat This Role Is (and Is Not)\n'14; Deeply hands-on technical leadership\n'14; Python-first engineering\n'14; Production-grade RAG ownership\n'14; Mentorship through real code\n'16; Not a research-only role\n'16; Not a people-manager-only role\n'16; Not a demo or prototype position