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Title: Digital Health and AI Specialist
Location: Remote
Company Background
At APV, we’re more than a technology company — we’re a mission\-driven powerhouse transforming organizations through advanced technology and human ingenuity. Our expertise spans AI/ML, data architecture, low\-code/no\-code development, Agile DevSecOps, and cloud services, delivering scalable and meaningful solutions. In our Emerging Technology Lab, innovation drives progress. Our teams create intelligent chatbots, AI\-powered assistants, robotic process automation (RPA), essay graders, and data analytics platforms. If you’re passionate about solving complex challenges and shaping the future, APV is the place for you.
Since 2007, we’ve partnered with federal and state agencies to deliver IT, training, and consulting solutions that achieve mission\-critical outcomes. Built on accountability, integrity, and quality, we go beyond expectations.
With 70\+ prime contracts and a proven record of client success, APV continues to grow — and we’re looking for exceptional talent to grow with us.
At APV, we Always Provide Value.
Role:
The Digital Health and AI Specialist is responsible for the technical content integrity of all artificial intelligence, digital health, interoperability, wearable technology, and health IT technical assistance delivered under the program. This individual serves as the lead subject matter expert on healthcare technology adoption and implementation, helping healthcare organizations understand, evaluate, and integrate emerging technologies into clinical and operational workflows. The Digital Health and AI Specialist bridges clinical and technical domains and translates complex concepts into practical, actionable guidance for diverse audiences.
Duties:
The Digital Health and AI Specialist will:
Digital Health \& AI Technical Leadership
- Serve as the primary subject matter expert for digital health technologies, artificial intelligence applications, interoperability, and health information exchange.
- Develop and review technical assistance materials, implementation guides, webinars, and educational resources related to healthcare technology adoption.
- Provide guidance on the responsible use of AI in healthcare, including governance, risk management, transparency, and bias mitigation practices.
Health IT \& Interoperability Support
- Advise healthcare organizations on EHR optimization, interoperability, and integration of digital health tools into existing workflows.
- Provide technical guidance related to HL7, FHIR, and the exchange of healthcare data across systems and platforms.
- Support implementation strategies that improve access to, utilization of, and actionability of clinical and operational data.
Wearable Technology \& Patient\-Generated Health Data
- Provide expertise on integrating patient\-generated health data (PGHD) from wearable devices and remote monitoring technologies into care delivery models.
- Advise on the use of technologies such as Self\-Measured Blood Pressure (SMBP) devices, Continuous Glucose Monitors (CGMs), and other digital health solutions that support chronic disease management.
- Assist organizations in identifying best practices for collecting, analyzing, and utilizing patient\-generated data to support informed decision\-making.
Technical Assistance \& Collaboration
- Deliver technical assistance through webinars, workshops, Communities of Practice, consultations, and other engagement activities.
- Collaborate with clinical, programmatic, and evaluation teams to ensure alignment between technical guidance and program objectives.
- Monitor emerging trends, technologies, and industry best practices to inform technical assistance and resource development efforts.
Education:
- Master's degree in Health Informatics, Biomedical Informatics, Computer Science, Digital Health, or a closely related field.
- Relevant industry certifications, such as CAHIMS, CHTS, or equivalent, may be considered in lieu of an advanced degree when combined with substantial relevant experience.
Required Experience and Skills:
- Minimum seven (7\) years of experience in digital health, health informatics, or AI implementation in healthcare settings.
- Demonstrated experience with EHR systems commonly used in community healthcare environments, HL7/FHIR standards, and wearable device or patient\-generated health data integration.
- Demonstrated knowledge of responsible AI principles, algorithmic bias mitigation, and AI governance frameworks in healthcare.
- Direct experience with Self\-Measured Blood Pressure (SMBP), Continuous Glucose Monitor (CGM) use for Type 2 Diabetes, and incorporation of patient\-generated health data from wearable devices into clinical care.
- Ability to communicate complex technical concepts to clinical, operational, and non\-technical audiences.
- Strong written, presentation, and facilitation skills
Preferred Skills:
- Experience supporting federal healthcare programs or federally funded health initiatives.
- Experience providing technical assistance, training, or consulting services to healthcare organizations serving underserved populations.
- Familiarity with health information exchange, population health technologies, remote patient monitoring, and digital care delivery models.
- Experience evaluating, implementing, or governing AI\-enabled healthcare solutions.
- Knowledge of healthcare data privacy, security, and interoperability best practices.
- Experience working with multidisciplinary teams that include clinicians, technologists, and healthcare administrators.
About APV
APV is an Equal Employment Opportunity employer. All qualified applicants are considered without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, or marital status.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At A P Ventures LLC, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
A P Ventures LLC AI Hiring
A P Ventures LLC 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 $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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