Lead AI Engineer

Job Description & Summary The opportunity Provide hands-on engineering leadership for agentic AI products, define implementation patterns and ensure technical quality from prototype through production. What you will be doing ·        Lead technical design and implementation of agents, RAG services, tool integrations and model orchestration. ·        Establish coding, testing, evaluation, review and documentation standards. ·        Decompose architecture into engineering work and guide estimation and sprint planning. ·        Coach engineers, review code and resolve complex technical problems. ·        Design evaluation suites for quality, safety, reliability, latency and cost. ·        Work with architects and MLOps to harden solutions for production. What we need from you ·        6+ years in software, data or machine-learning engineering, including hands-on AI delivery. ·        Strong Python and API engineering capability and experience with modern agent or LLM frameworks. ·        Experience with retrieval, embeddings, vector stores, model evaluation and distributed systems. ·        Ability to lead agile engineering teams while remaining hands-on. Relevant AI technologies and tooling ·        Strong hands-on expertise in Python and API engineering, with production experience using agent frameworks such as LangChain and LangGraph, Microsoft Agent Framework or Semantic Kernel, OpenAI Agents SDK, AutoGen, CrewAI, or equivalent. ·        Ability to implement graph-based and code-first orchestration patterns, including state, memory, checkpoints, tool calling, hand-offs, retries, idempotency, human approval and long-running workflows. ·        Advanced experience with RAG, structured outputs, prompt and context engineering, embeddings, vector or hybrid retrieval, reranking, knowledge graphs and retrieval evaluation. ·        Experience integrating agents with enterprise systems through REST or GraphQL APIs, events, queues, databases and MCP-compatible tools or servers. ·        Practical experience with automated evaluation and observability using technologies such as LangSmith, MLflow, Langfuse, OpenTelemetry, Azure AI evaluation capabilities or equivalent, covering quality, trajectory, latency, token use and cost. ·        Strong software-engineering discipline across pytest or equivalent testing, type checking, code review, dependency management, secure coding, CI/CD and containerized deployment. Measures of success ·        Engineering throughput and predictability ·        Code quality and automated test coverage ·        Evaluation performance and production readiness ·        Reduction of defects and rework ·        Development of reusable components Key interfaces ·        Other members of the AI Transformation & Agentic Systems Practice ·        PwC sector, functional, cloud, cyber, risk, Responsible AI and change specialists ·        Client business owners, product owners, technology teams and operational users ·        Technology alliance and implementation partners where relevant Contribution to the practice ·        Support proposals, client workshops and market development appropriate to seniority. ·        Contribute reusable methods, patterns, code, assets and lessons learned. ·        Coach colleagues and participate in the capability’s continuous learning agenda. ·        Uphold PwC quality, independence, confidentiality and risk-management requirements. #LI-BS1 #LI-Hybrid