AI Solutions Engineer

Job Description

The AI Solutions Engineer is a critical member of the Knowledge and Process Management team at IPM, serving in a hands‑on engineering role that spans both strategy and implementation. This role bridges technology and operations, translating emerging AI capabilities into practical tools and scalable solutions that enhance both internal effectiveness and client delivery. By accelerating productivity and strengthening consulting offerings, the AI Solutions Engineer plays a key role in establishing a competitive advantage by building and deploying AI capabilities across data pipelines, security, evaluation frameworks, and operational patterns.

Core areas of responsibility include production hardening, platform development, security implementation, data foundations, and operational enablement.

In close partnership with IPM’s AI Development Leads, the AI Solutions Engineer will identify high-impact opportunities for AI adoption, rapidly prototype solutions, and drive them through to production. Solutions are expected to meet enterprise standards for security, observability, and integration with IPM’s M365 and Azure environments, while also being robust and scalable enough to support long‑term use across the firm.

The position requires close collaboration with stakeholders across Operations, Corporate functions, IT, and Practice Areas to identify high‑value use cases and deliver solutions aligned with business priorities. In addition, the role supports adoption through training, knowledge sharing, and governance. Responsibilities include AI strategy and opportunity identification, AI solution development, AI enablement and service development, AI solution delivery, and emerging technology governance.

The balance between internal initiatives and client‑facing work will evolve over time, based on business priorities, client needs, and the maturity of IPM’s AI capabilities.

• Identify and prioritize opportunities for AI‑enabled solutions across internal operations and client engagements.
• Engage stakeholders (internal and external) to understand workflows, pain points, and value drivers.
• Design, develop, test, and deploy AI‑enabled solutions that support internal operations.
• Build and maintain CI/CD pipelines and infrastructure‑as‑code; manage dev, staging, and production environments.
• Implement authentication, authorization, and data access controls (Entra , role‑based access, tenant isolation where applicable).
• Curate and maintain internal knowledge bases for retrieval‑grounded systems.
• Handle sensitive data appropriately in AI workflows (PII, client‑confidential, regulated data).
• Enhance knowledge capture, accessibility, and reuse across client engagements and internal operations.
• Drive consistency and scalability of core IPM processes through AI.
• Support the evolution of IPM’s consulting practices through the application of AI and emerging technologies.
• As a part of client engagements, design and deliver AI‑enabled solutions and tools, partner with engagement leads to define technical scope, integration constraints, and handoff expectations, and translate IPM's internal production patterns to client‑side deployments.
• Evaluate new AI tools and capabilities for potential application within IPM by leading pilots and implementation of new technologies.
• Implement AI‑specific security controls and support ongoing maintenance monitoring and continuous improvement of deployed internal solutions.
• Develop resources, playbooks, and guidance to support effective and consistent use of AI tools.
• Deliver training and enablement for both internal teams and client stakeholders.
• Promote practical adoption through hands‑on support, working sessions, and knowledge sharing.
• Support client‑facing training, workshops, and enablement activities as part of AI‑enabled engagements.

• Bachelor’s degree in engineering, computer science, data science, or a related field.
• 3+ years of experience in software development, data science, or AI/ML roles.
• Exposure to production AI application architecture: retrieval patterns (RAG, agentic retrieval), agent orchestration, prompt…