Lead AI Engineer

Job Description & Summary

Provide hands-on engineering leadership for agentic AI products define implementation patterns and ensure technical quality from prototype through production.

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.

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.

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.

Engineering throughput and predictability

Code quality and automated test coverage

Evaluation performance and production readiness

Reduction of defects and rework

Development of reusable components

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

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 capabilitys continuous learning agenda.

Uphold PwC quality independence confidentiality and risk-management requirements.

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