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About This Role
Overview:
As an AI Program/Project Manager, you will play a pivotal role in driving the development and deployment of innovative AI/ML solutions. You will collaborate closely with cross\-functional teams, including engineers, data scientists, and business stakeholders, to identify, prioritize, and deliver high\-impact AI/ML products. You are data driven and influence our business strategy and technical priorities with that vision. This role will drive alignment between data engineering, AI, and business teams, ensuring successful execution of our data and gen AI initiatives.
Responsibilities:
- Collaborate with cross\-functional teams to define the AI/ML solution vision, scope, dependencies and risks.
- Partner with Engineering to develop and maintain the solution roadmap, prioritized backlog, and clear deliverables aligned with project objectives and key results.
- Lead the delivery of high\-quality AI/ML solutions, ensuring adherence to timelines, scope, and quality standards to deliver impactful solutions.
- Own and prioritize the AI/ML solution roadmap to deliver high\-value MVPs quickly.
- Lead efforts to gather and communicate progress, metrics and key results to leadership, executive team, internal partners and other stakeholders via presentations and reporting.
- Represent the program and team, effectively communicating with executives and stakeholders.
- Collaborate with business stakeholders to deeply understand their needs and challenges, translating them into clear problem statements and objectives.
- Work with R\&D and Operations to identify business problems, evaluate the potential of AI/ML solutions, and assess associated risks and benefits.
- Partner with Operations to ensure seamless deployment of models into production environments and provide ongoing support for model performance monitoring and optimization.
- Work with other data science teams to share best practices, collaborate on projects, and support broader data science initiatives across the organization.
- Drive data\-driven decision\-making, utilizing data analytics to optimize program performance and achieve strategic objectives.
- Complete all responsibilities as outlined in the annual performance review and/or goal setting.
- Complete all special projects and other duties as assigned.
- Must be able to perform duties with or without reasonable accommodation.
This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.
Qualifications:
- Bachelors or Master’s degree in Engineering, Computer Science, or other technical related field.
- 5\+ years of technical product/program management experience with 4\+ years delivering data product solutions within an AI/ML solutions portfolio preferably in a healthcare domain.
- Data driven program manager with skills in product requirements that are concise, with clear, measurable success criteria.
- Strong understanding of data engineering and AI/ML project lifecycles and data platform ecosystems (Databricks experience a plus).
- Excellent communication skills, ability to make informed trade\-offs, and a data\-driven approach to planning and execution.
- Experience in translating complex technical concepts into clear and understandable language for both technical and non\-technical audiences.
- Driven and energetic, with a strong foundation in data engineering and and product strategy.
- Proven experience leading cross\-functional teams and delivering results.
- Collaborative and curious with a passion for driving value through AI/ML and data\-driven solutions.
- Results\-oriented, measuring success by the value delivered, not the hours worked.
- Proficiency in all required skills and competencies above.
Cognitive/Mental Requirements:* Foster a collaborative, learning\-oriented culture that unites all facets of product development.
- Effective communication and teamwork skills.
- Proven ability to handle sensitive information with discretion and professionalism.
- Strong organizational and time management skills, with the ability to prioritize tasks and meet deadlines.
- Ability to work both independently and collaboratively, with a strong sense of urgency.
Working Conditions and Physical Requirements:* Flexibility to work effectively with diverse teams across different time zones and geographical locations.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide a dedicated, secure work area.
- Must be able to provide high\-speed internet access / connectivity and office setup and maintenance.
Base compensation ranges from $110,000 to $135,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.
Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page.
Since this job will be based remotely, all interviews will be conducted virtually.
Date of posting: 4/1/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 7/01/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
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Salary Context
This $110K-$135K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cotiviti, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($122K) sits 27% below the category median. Disclosed range: $110K to $135K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety, AI Software Engineer. Based in Remote, US. Compensation range: $124K - $180K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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