AI Engineer

The AI Engineer designs, builds, deploys, and supports practical AI solutions that solve business problems and deliver measurable value. This role partners with business leaders and subject matter experts to understand workflows, identify high-value opportunities, and translate ambiguous needs into secure, scalable, and maintainable technical solutions.

The position combines business-facing consulting with hands-on engineering, with a significant focus on designing, developing, integrating, testing, deploying, and supporting AI solutions. Solutions may range from low-code agents and automations to custom-developed Azure applications and integrations. The AI Engineer selects and implements the approach that best fits the business and technical requirements. The AI Engineer uses Microsoft Copilot Studio, Azure, APIs, connectors, code, and other approved enterprise technologies, and treats AI solutions as production software by applying established software engineering and SDLC practices throughout their lifecycle.

Success requires both strong communication skills and sound engineering judgment. The ideal candidate can work comfortably with non-technical stakeholders while applying software engineering principles, AI governance, security, source control, testing, documentation, and operational support practices.

ESSENTIAL JOB RESPONSIBILITIES include but are not limited to:

• Supports and demonstrates IMA’s core values
• Values and understands the importance of diversity, equity, and inclusion among all IMA associates

Business Partnership & Solution Design

• Partner with the AI Enablement Lead and business stakeholders to understand problems, workflows, users, data dependencies, risks, and success criteria.
• Assess technical feasibility and translate business needs into requirements, solution architecture, user stories, delivery plans, and measurable outcomes.
• Communicate options, constraints, tradeoffs, and recommendations to business and technical audiences. The AI Enablement Lead primarily drives business engagement, prioritization, and adoption; the AI Engineer leads technical design and delivery.

AI Architecture & Engineering

• Design, build, test, deploy, and maintain AI agents, copilots, workflow automations, integrations, and AI-enabled applications.
• Develop solutions using Microsoft Copilot Studio, Azure services, APIs, connectors, Python and/or C#, and other approved technologies.
• Design integrations with enterprise applications, knowledge sources, and structured or unstructured data.
• Develop prompts, agent instructions, grounding strategies, tools, connectors, and reusable components.
• Apply RAG, semantic/vector search, embeddings, agentic patterns, and model orchestration when appropriate.
• Own or substantially contribute to architecture decisions, including Copilot Studio versus custom Azure solutions. Consider Azure AI Foundry capabilities when relevant and balance value, security, cost, scalability, and maintainability.

SDLC, Operations & Quality

• Apply SDLC practices across requirements, architecture, development, code review, testing, release management, documentation, support, and retirement.
• Use source control, CI/CD, environment separation, configuration management, and repeatable deployment practices.
• Implement appropriate unit, integration, functional, regression, security, and user-acceptance testing.
• Build in logging, monitoring, usage analytics, performance measures, and alerts; troubleshoot production issues and perform root-cause analysis.
• Create architecture diagrams, technical specifications, runbooks, support documentation, and user guidance.

Governance, Security & Value

• Implement controls for identity and access, data privacy, human oversight, source grounding, output validation, and auditability.
• Evaluate and mitigate hallucinations, prompt injection, unsafe or inaccurate outputs, bias, data leakage, and inappropriate tool actions.
• Assess knowledge and data readiness, and partner with content owners to improve quality, permissions, organization, and lifecycle management.
• Partner with the AI Enablement Lead on pilots, rollout, user enablement, adoption, feedback, and measurement of business outcomes.
• Monitor solution reliability and usage, prioritize engineering enhancements, and contribute to the AI delivery backlog and roadmap.

REQUIRED EXPERIENCE AND SKILLS include but are not limited to:

• Bachelor’s degree in Computer Science, Eng…