AI Enablement Engineer

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Full Time Professional Independence, OH, US

Supervised by: Director of Software Development

FLSA Status: Exempt / Full-Time

The AI Enablement Engineer is a senior-level, hands-on technical leader responsible for enabling the rapid development of secure and scalable AI capabilities across enterprise products. This role specializes in LLMs, prompt engineering, and AI-driven automation techniques, collaborating closely with rapid prototyping developers, architects, and security experts to ensure AI solutions integrate effectively with enterprise systems. This role will directly improve implementation velocity by automating data mapping, loading, validation and related workflows and will champion responsible enterprise adoption of AI tools and operational workflows across the organization.

• AI Platform Evaluation and Solution Design
• Evaluate, recommend, and integrate LLMs and AI tooling, including platform selection guidance (e.g., OpenAI, Anthropic, Azure OpenAI, Hugging Face, and comparable enterprise AI platforms / tools). When AI tooling affects client onboarding, data migration, or conversion workflows, partner with Implementation Services and Data Engineering leadership to assess feasibility and operational impact.
• Provide technical evaluation input on feasibility, security posture, architecture fit, integration complexity, and total cost of ownership within a build vs. buy vs. partner framework owned jointly by Product Management and Implementation Services leadership. This role informs the business case; it does not own ROI sign-off.
• Collaborate with technical architecture to ensure AI solutions align with enterprise patterns, data flows, and security/compliance frameworks.
• AI Enablement and Workflow Automation
• Partner directly with the rapid prototyping developer to shape and refine AI-driven product features for feasibility, scalability, and quality, with primary focus on AI-augmented implementations.
• Develop advanced prompt engineering techniques, reusable AI patterns, and orchestration methods to accelerate implementation services. Document and standardize repeatable approaches so they can be adopted at scale.
• Apply generative AI to automate testing, linting, fuzz testing, and technical debt reduction.
• Operationalize autonomous coding workflows (Git Hub Copilot, Claude Code) with governance guardrails, review gates, and productivity metrics.
• Serve as the hands-on enablement lead for Implementation Services; building reference workflows, training delivery staff, and removing adoption friction so tooling produces measurable outcomes.
• Partner with Implementation Services leadership to document current-state data onboarding: source systems/formats (e.g., mainframe extracts, EHR exports, claims files), per-client cycle time, and where the pain concentrates (mapping logic, validation, testing, or all three).
• Security, Privacy and Compliance
• Evaluate and deploy PHI-safe LLM options (Claude Enterprise and/or local models), aligning handling with HITRUST r2 controls and HIPAA/HITECH obligations.
• Design and enforce guardrails for secure handling of PHI and PII within AI sessions and pipelines.
• Adoption, Training, and Change Enablement
• Establish and report adoption and impact metrics, including cycle-time reduction, mapping accuracy, defect rates, and adoption rate, suitable for SLT and board-level visibility.
• Lead enablement activities that drive adoption across technical and delivery teams, including training, office hours, documentation, and workflow reinforcement.
• Production Readiness, Knowledge Sharing and Continuous Improvement
• Provide AI mentorship to senior development staff and Implementation Services delivery teams via pairing sessions, design reviews, and lunch-and-learn sessions.
• Create and collaborate on architectural blueprints, compliance/security standards, reusable frameworks, and reference implementations for AI projects.
• Support transition of prototypes into production-ready, enterprise-compliant solutions.

• Deep technical expertise in AI/LLM engineering.
• Strong problem-solving, analytical thinking, and attention to detail.
• Ability to translate complex AI concepts into actionable, scalable solutions.
• Effective mentorship and knowledge-sharing abilities with senior developers.
• Ability to influence and drive…