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