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 Lang. Chain and Lang. Graph, Microsoft Agent Framework or Semantic Kernel, OpenAI Agents SDK, Auto. Gen, 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 Lang. Smith, MLflow, Langfuse, Open. Telemetry, 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