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.

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