AI Enablement Engineer

MediQuant LLC•Independence, OH

1d•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred.
5+ years of progressive AI/ML engineering, including recent, hands-on LLM development and deployment work.
Deep understanding of LLM internals (tokenization, context windows, reasoning behaviors).
Experience with model fine-tuning, evaluation, and optimization.
Security and privacy best practices for PHI/PII data protection in AI workflows.
Strong skills in prompt engineering and orchestration frameworks (LangChain, vector search, semantic caching).
Familiarity with NLP, OCR/ML, and document processing where applicable.
Strong data engineering fundamentals: ETL/ELT, schema mapping, transformation, and loading at scale.
Healthcare data experience; working knowledge of HIPAA/HITECH, PHI handling, and HITRUSTaligned controls.
Practical experience with AI coding assistants (Copilot, Claude Code, or equivalent) in a governed environment.
Demonstrated ability to drive adoption, translating technical capability into workflows that are sustainable.
Practical experience using generative AI for test automation and code quality improvements.
Strong collaboration and communication skills to work effectively across architecture, development, and security teams.

Nice To Haves

Master’s degree preferred.

Responsibilities

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.
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 (GitHub 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).
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.
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.
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.