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Overview:
We are seeking a highly accomplished Principal AI Process Specialist to lead enterprise-critical process improvement initiatives that drive strategic growth and operational transformation. Looking for a senior leader with a proven track record of delivering significant process improvements with financial impact of seven to eight figures.
The Principal AI Process Specialist will lead cross-functional digital transformation and process improvement initiatives with high executive visibility, driving adoption of technical solutions across functions including Finance, Operations, Sales, Marketing, Product, IT, Data Science, Legal, Compliance, R&D, Legal, HR, and other areas.
The role applies the right mix of methodologies and tools—including process mining, AI/ML, technical documentation, change management, data-driven optimization, and AI process improvements—based on project needs, with or without formal DMAIC/DMADV structure. Core responsibilities include technical process documentation, organizational readiness, change execution, and AI-enabled process improvement implementations. This position is highly self-directed and accountable for independently driving projects through adoption.
Responsibilities:
- Lead high-impact digital transformation and process improvement projects, typically delivering significant business value through AI, automation, and analytics, with seven to eight figure financial impact.
- Identify, evaluate, and implement AI/ML and intelligent automation solutions to optimize business processes.
- Develop and maintain technical process documentation, including process flows, RACI charts, integration diagrams, SOPs, and design artifacts.
- Assess organizational readiness, identify change impacts, and execute change management strategies to support adoption of new technologies and processes.
- Lead stakeholder engagement, including impact assessments, communication planning, training needs analysis, and resistance management.
- Conduct process discovery using tools such as process mining, value stream mapping, analytics, and interviews to identify improvement opportunities.
- Provide project leadership for initiatives such as integrations, system implementations, and strategic transformation efforts as assigned.
- Coordinate across business and technical teams to align scope, requirements, timelines, handoffs, and deliverables.
- Deliver executive updates on project status, ROI, feasibility, readiness, and adoption metrics.
- Adapt process improvement approaches to project needs using Lean, Six Sigma, DevOps, or hybrid methods.
- Build and maintain process repositories, knowledge assets, dashboards, and measures that support continuous improvement.
- Support training, mentoring, and certification efforts in process improvement, technical writing, change management, and AI literacy.
- Complete other duties and special projects as assigned, implementations, and strategic transformation efforts as assigned.
- Complete all responsibilities as outlined on annual Performance Plan.
- Complete all special projects and other duties as assigned. Required.
- 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 the requirements of the job change. Qualifications:
- 10+ years leading digital transformation, process improvement, technology implementation, or complex operational initiatives in large, matrixed organizations.
- Experience in health care or health care-adjacent companies.
- Consulting experience preferred.
- Experience partnering with Finance, including familiarity with budgeting, financial reporting, business metrics, executive-level reporting, and developing financial impact analysis.
- Experience working with globally distributed teams in remote environments.
- Demonstrated ability to manage multiple concurrent large initiatives with competing priorities, tight timelines, and evolving business needs is a must.
- Proven success scoping and delivering enterprise projects with measurable outcomes and executive visibility.
- Strong experience in technical process documentation, organizational change management, and executive stakeholder engagement.
- Bachelor’s degree in Computer Science, Business, Engineering, Information Systems, a health or medical discipline, or a related field; MBA or equivalent business and technology knowledge required.
- Required certifications: Change Management (Prosci, ACMP, or equivalent) and Lean Six Sigma Black Belt or Master Black Belt.
- Preferred certifications: PMP and relevant technical credentials in cloud platforms, analytics, AI/ML, or Microsoft Power Platform.
- Working knowledge of AI/ML, process mining, intelligent automation, technical documentation tools, visualization platforms, and Microsoft Power Platform.
- Strong communication, technical writing, stakeholder management, problem-solving, and ability to translate technical concepts for non-technical audiences.
- Highly organized, self-directed, and able to manage multiple priorities in a fast-changing environment characterized by ambiguity, change, and evolving complexity.
- Thought leadership background and experience introducing new tools and processes in a large corporate environment.
- Travel under 10%; remote work environment with virtual collaboration across global teams.
Cognitive/Mental Requirements:
- Ability to work independently as well as collaborate as a team with a sense of urgency.
- Professional with ability to properly handle confidential information.
- Be value-driven, understand that success is based on the impact of your work rather than its complexity or the level of effort.
- Ability to handle multiple tasks, prioritize and meet deadlines.
- Ability to work within a matrixed organization.
- Proficiency in all required skills and competencies above.
- Communicating with others and teamwork.
- Assessing the accuracy, neatness, and thoroughness of the work assigned.
Working Conditions and Physical Requirements:
- Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- No adverse environmental conditions expected.
- Must be able to provide high-speed internet access / connectivity and office setup and maintenance.
- Must be able to provide a dedicated, secure work area.
Base compensation ranges from $160,000 to $180,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This role is eligible for discretionary bonus consideration.
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: 9/24/2026 Applications are assessed on a rolling basis. We anticipate that the application window will close on 11/24/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected. #LI-Remote
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Salary Context
This $160K-$180K 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 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 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 $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 ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
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.
Cotiviti AI Hiring
Cotiviti has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Engineer. Based in Remote, US. Compensation range: $155K - $180K.
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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