AI Engineer, Forward Deployed (Copilot and Azure AI Foundry)

Collectiv•Chicago, IL

1d•Remote

About The Position

The AI Engineer is a hands-on individual contributor who works directly with client teams to design, build, and ship production AI solutions using Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry (Microsoft Foundry). The engineer writes code, configures agents, connects data, and prepares solutions for real users, working within a Collectiv delivery team alongside a project lead, architects, and data engineers. They partner with client business and IT stakeholders to translate business problems into working AI solutions and harden them for production. The role includes building Copilot agents, connectors, plugins, and workflows; developing custom agents and AI applications in Azure AI Foundry; creating retrieval-augmented generation pipelines; establishing evaluation, monitoring, security, and governance practices; and supporting client adoption. The engagement described requires US citizenship under the client’s contract terms and physical presence in the United States for the duration of the engagement. These conditions apply to this specific client project, not to Collectiv generally.

Requirements

Bachelor’s degree from an accredited university in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
At least 4 years of experience in software engineering, data engineering, or Microsoft 365 and Power Platform development.
At least 1 year of hands-on experience building and deploying generative AI or LLM-powered solutions in production or client environments.
Hands-on experience with Microsoft 365 Copilot extensibility and Copilot Studio.
Hands-on experience with Azure AI Foundry or Azure OpenAI.
Experience building RAG solutions with Azure AI Search.
Strong Python or C# skills.
Experience working in Azure, including storage, compute, identity, and networking.
Ability to work independently in a client-facing consulting role.
US citizenship is required for this specific client engagement.
Must be physically located in the United States for the duration of the engagement.

Nice To Haves

Experience with Microsoft Agent Framework, Semantic Kernel, or AutoGen.
Experience with Power Platform, Dataverse, and Power Automate.
Experience with Microsoft Fabric, Databricks, or Power BI.
Experience with LLMOps practices, including evaluation frameworks, prompt management, and model monitoring.
Familiarity with security, privacy, and compliance considerations for AI in regulated industries.
Consulting experience across multiple clients or industries.
Relevant certifications such as Azure AI Engineer Associate, Copilot Studio or Power Platform certifications, or Fabric certifications (DP-600, DP-700).

Responsibilities

Build declarative agents, Copilot connectors, and plugins that extend Microsoft 365 Copilot with client data and workflows.
Build Copilot Studio agents, topics, actions, and autonomous agents for business processes.
Connect Copilot to SharePoint, Teams, Microsoft Graph, Dataverse, and line-of-business systems.
Write and iterate on prompts, instructions, and agent configurations based on test results and user feedback.
Build custom agents and AI applications in Azure AI Foundry for use cases beyond out-of-the-box Copilot.
Select, deploy, and configure models from the Foundry model catalog and explain trade-offs in cost, quality, and latency.
Implement agentic workflows with tool calling, multi-step orchestration, and human-in-the-loop controls.
Integrate AI solutions with enterprise systems through APIs, Azure Functions, Power Automate, and Logic Apps.
Build end-to-end RAG pipelines, including ingestion, chunking, embedding, vector and hybrid search, and grounding.
Use Azure AI Search and Azure AI Document Intelligence to index and extract knowledge from client content.
Partner with Collectiv data engineers to connect AI solutions to Microsoft Fabric, Databricks, and other enterprise data platforms.
Build evaluation sets and test harnesses to measure answer quality, groundedness, hallucination rate, safety, latency, and cost.
Implement logging, monitoring, and tracing for deployed agents and models.
Apply security, identity, and governance controls using Microsoft Entra ID, Microsoft Purview, and Azure AI Content Safety.
Use version control, CI/CD, and code review standards for AI solution code and configuration.
Work directly with client stakeholders to gather requirements, run working sessions, demonstrate progress, and manage expectations.
Translate business problems into scoped, buildable AI use cases with clear success metrics.
Train client users and administrators on solutions and support adoption after go-live.
Contribute reusable patterns, accelerators, and documentation to Collectiv’s AI practice.