Senior Associate, Azure AI Engineer

PricewaterhouseCoopers•Chicago, IL

22h

About The Position

As a Senior Associate - Azure AI Engineer, you will design and develop generative AI solutions for clients within PwC’s Advisory practice, applying Azure-based engineering to application development and modernization. You will shape AI architectures from analytical modeling and prompt engineering through testing, deployment, and maintenance. Using Python, Azure AI Services, and large language model APIs, you will build applications that use retrieval-augmented generation and vector databases to ground responses in client data. You will also develop agent workflows with LangGraph to coordinate models, tools, and application components in addressing business challenges. You will own assigned workstreams and deliverables, using critical thinking when technical choices or client requirements are unclear. You will build client connections by listening to different perspectives, anticipating needs, and explaining tradeoffs in the context of broader business goals. You will guide junior colleagues and review their work while maintaining professional and technical standards. Through feedback and reflection, you will deepen your technical skills, adapt to changing project conditions, and build your professional reputation through dependable delivery. Travel requirements are up to 80%.

Requirements

At least a bachelor's degree.
At least 3 years of experience.

Nice To Haves

Experience building generative AI applications with Azure AI Services, Azure OpenAI Service, and LLM APIs for client projects.
Experience orchestrating AI agent workflows with LangGraph and Model Context Protocol, including tool integration and error handling.
Experience developing retrieval-augmented generation pipelines with vector databases and context engineering.
Experience evaluating LLM outputs and applying guardrails during prompt engineering, testing, and application development.
Experience deploying Python or C# microservices on Microsoft Azure using DevOps practices, Azure Kubernetes Service, and application monitoring.
Experience guiding junior team members through technical reviews while communicating design tradeoffs and navigating changing client priorities.

Responsibilities

Design Azure-based generative AI architectures and analytical models that turn client data into actionable insights and translate business needs into technical deliverables.
Build conversational applications with Azure AI Services, Azure OpenAI Service, LLM APIs, prompt engineering, and context engineering to support client decision-making.
Develop retrieval-augmented generation pipelines using data modeling, information retrieval systems, and vector databases to ground model responses in source data.
Orchestrate AI agents with LangGraph, LangChain, and Model Context Protocol, coordinating tool access, shared state, and error handling across workflows.
Create cloud-native microservices in Python, C#, and JavaScript, integrating AI capabilities with existing applications through Azure API Management and enterprise service buses.
Apply GitHub Copilot and Claude Code across development and testing, using DevOps and LLMOps practices to deploy and maintain solutions on Azure Kubernetes Service.
Evaluate model outputs and application behavior through LLM evaluation, guardrails, and performance monitoring, while applying identity, access, and key management controls.
Collaborate with clients and colleagues to clarify requirements, manage assigned workstreams, review deliverables, mentor junior team members, and incorporate feedback throughout the development life cycle.