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About This Role
POSITION TITLE: AI Enablement Engineer
Supervised by: Director of Software Development
Supervises: N/A
FLSA Status: Exempt / Full-Time
POSITION SUMMARY. 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.
JOB DUTIES and ESSENTIAL FUNCTIONS. A qualified individual must be able to perform these essential functions of the job as listed, with or without accommodation.
- 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 (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).
- 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.
QUALIFICATIONS
Competencies:
- 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 adoption across cross-functional stakeholders.
Required Education and Experience:
- 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.
Work Environment. Employees may work from our corporate headquarters or work from home. Employees may at times be required to travel to headquarters for meetings or company events. Remote workers will be provided with a company laptop and are responsible for maintaining access to highspeed internet.
Physical Demands. While performing the duties of this job, the employee is regularly required to talk or hear. The employee frequently is required to sit for long periods of time, stand; walk; use hands to finger, handle or feel; and reach with hands and arms. The employee is occasionally required to climb or balance; and stoop, kneel, crouch or crawl. The employee must occasionally lift and/or move up to 10 pounds and occasionally lift and/or move up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception and ability to adjust focus.
Position Type and Expected Hours of Work. This is a full-time position, and the hours of work and days are typically Monday through Friday, 8:30 a.m. to 5 p.m. Some flexibility in hours is allowed, but the employee must be available during the “core” work hours of 9:00 a.m. to 3:30 p.m. Occasional evening and weekend work may be required as job duties demand.
Travel. Incumbent may occasionally experience some out-of-the-area and overnight travel.
Work Authorization. In compliance with Federal employment laws, MediQuant will verify the identity and employment authorization of each person hired. MediQuant participates in the Federal E-Verify program.
Security Clearance. Must be able to pass all security clearances mandated by various government contracts as well as client hospital/healthcare security requirements. Any employee working on government projects will be required to successfully pass a government background check and receive a Common Access Card (CAC). Additionally, the applicant must be a U.S. citizen and will be subject to a Public Trust security background investigation and must meet requirements to obtain a Department of Defense (DOD) network account.
AAP/EEO Statement. MediQuant, Inc. is an equal opportunity employer.
Compliance. Employees shall comply with all MediQuant policies, state and federal laws, regulations, and contractual obligations when accessing MediQuant or client Confidential Data, Confidential Information, and Information Assets.
Other Duties. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At MediQuant, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
MediQuant AI Hiring
MediQuant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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