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About This Role
At Prime Therapeutics (Prime), we are a different kind of PBM, with a purpose beyond profits and a unique ability to connect care for those we serve. Looking for a purpose-driven career? Come build the future of pharmacy with us.
Job Posting Title
AI Strategist PrincipalJob Description
The Principal Artificial Intelligence (AI) Strategist will help lead and scale enterprise AI adoption, governance, and value realization across the organization. This role serves as a trusted advisor within Prime's AI function and works closely with business, technology, operations, clinical, legal, privacy, security, and executive leaders. The Principal AI Strategist is responsible for accelerating the adoption of AI capabilities while ensuring alignment with enterprise strategy, governance requirements, risk management practices, and business outcomes. This strategic partner will help operationalize Prime's AI strategy, support the AI Steering Committee, facilitate AI governance processes, identify opportunities for AI-enabled transformation, and drive measurable value across business capabilities.
Responsibilities
- Partner with the VP of AI Strategy to execute Prime's multi-year AI strategy and roadmap, translating enterprise priorities into actionable plans, business cases, investment priorities, and informed recommendations based on industry, technology, vendor, and competitive trends
- Lead Prime's AI governance framework and operating model by managing governance forums, Steering Committee processes, risk-tier evaluations, cross-functional reviews, responsible AI practices, and recommendations related to regulatory and client requirements
- Drive enterprise AI enablement and adoption by partnering on programs, playbooks, communities of practice, champion networks, learning initiatives, and change management efforts while establishing metrics to measure utilization, adoption, and business impact
- Serve as a trusted advisor to business and technology leaders; identify, prioritize, and advance AI-enabled transformation opportunities that align with enterprise objectives, improve workflows, promote reuse of enterprise AI assets, and deliver measurable business outcomes
- Develop and manage AI value realization metrics, dashboards, and executive reporting, in partnership with Finance and business leaders; quantify productivity gains, operational improvements, financial benefits, and progress against enterprise targets
- Evaluate emerging AI vendors, platforms, strategic partnerships, and market developments to support informed enterprise decisions across healthcare, PBM, and AI capabilities
- Promote three-in-a-box collaboration across business, technology, and functional teams, and foster a culture of innovation, accountability, and responsible AI adoption
- Other duties as assigned
Minimum Qualifications
- Bachelor's degree in Business, Healthcare Administration, Computer Science, Information Technology, Data Science, or related area of study, or equivalent combination of education and/or relevant work experience; HS diploma from an accredited school or equivalent GED required
- 7 years of work experience in healthcare, PBM, technology, digital transformation or analytics including 2 years of work experience developing, implementing, governing or advancing enterprise AI
Must be eligible to work in the United States without the need for work visa or residency sponsorship
Additional Qualifications
- Understanding of AI, machine learning, generative AI, automation, and emerging digital technologies
- Demonstrated ability to connect technology investments to measurable business outcomes
- Familiarity with Microsoft Copilot, Azure AI, enterprise AI platforms, agentic AI solutions, and healthcare AI applications
- Excellent executive communication and presentation skills
Preferred Qualifications
- Master's degree (MBA, MHA, MS, or related field)
- PBM, health plan, healthcare services, or healthcare technology experience
- Experience leading AI Centers of Excellence, innovation teams, or transformation offices
- Experience working with executive leadership and cross-functional stakeholders
- Experience with governance, risk management, and regulatory compliance
Every employee must understand, comply with and attest to the security responsibilities and security controls unique to their job, and comply with all applicable legal, regulatory, and contractual requirements and internal policies and procedures
Every employee must be able to perform the essential functions of the job and, if requested, reasonable accommodations will be made to enable employees with disabilities to perform the essential functions, absent undue hardship. In addition, Prime retains the right to change or assign other duties to this job
Potential pay for this position ranges from $124,000.00 - $211,000.00 based on experience and skills. Prime Therapeutics LLC is proud to be an equal opportunity and affirmative action employer. We encourage diverse candidates to apply, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sex (including pregnancy), national origin, disability, age, veteran status, or any other legally protected class under federal, state, or local law.
We welcome people of different backgrounds, experiences, abilities, and perspectives including qualified applicants with arrest and conviction records and any qualified applicants requiring reasonable accommodations in accordance with the law.
Prime Therapeutics LLC is a Tobacco-Free Workplace employer.
Positions will be posted for a minimum of five consecutive workdays.
Salary Context
This $124K-$211K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Prime Therapeutics, 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $124K to $211K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Prime Therapeutics AI Hiring
Prime Therapeutics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $211K - $211K.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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