AI Engineer

Job Description & Summary The opportunity Build, test and integrate production-grade AI agents and services that execute business tasks reliably within enterprise workflows. What you will be doing · Implement agents, prompts, tools, retrieval pipelines and orchestration logic. · Integrate AI components with enterprise APIs, applications, databases and workflow services. · Build automated tests and evaluation datasets for functional and non-functional behavior. · Diagnose model, retrieval, tool-use and integration failures. · Contribute to secure coding, documentation, peer review and release activities. · Participate actively in agile ceremonies, demonstrations and backlog refinement. What we need from you · 3+ years in software, data or AI engineering. · Strong Python or comparable programming skills, API development and version control. · Practical experience with LLM applications, RAG, agents, embeddings and structured outputs. · Ability to work iteratively with product, architecture, data and user-experience specialists. Relevant AI technologies and tooling · Hands-on experience building agents with at least one production-oriented framework such as LangChain and LangGraph, Microsoft Agent Framework or Semantic Kernel, OpenAI Agents SDK, AutoGen, CrewAI, or equivalent. · Strong Python skills and practical experience with FastAPI or similar API frameworks, Pydantic or comparable schema validation, asynchronous programming, Git and automated testing. · Practical experience implementing tool calling, structured outputs, agent state and memory, hand-offs, guardrails, retries, human-in-the-loop steps and deterministic workflow nodes. · Experience implementing RAG pipelines using embeddings, vector or hybrid search, metadata filters, reranking and evaluation datasets. · Familiarity with MCP, enterprise API integration, queues or events, containerization with Docker and deployment to Kubernetes or managed application platforms. · Ability to instrument agent executions using tracing and evaluation tools such as LangSmith, MLflow, Langfuse, OpenTelemetry or platform-native equivalents. Measures of success · Working features delivered per iteration · Evaluation results and defect rates · Integration reliability · Code review and documentation quality · Contribution to reusable engineering assets 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