Interested in this AI/ML Engineer role at The Cigna Group?
Apply Now →About This Role
Senior Advisor, Clinical Product \& Clinical AI Enablement
*(Registered Nurse, Advanced Practice Provider, or Clinical Pharmacist)*
Company: The Cigna Group
Scope: Enterprise – Cigna Healthcare \& Evernorth
Reports to: Sr. Director, Enterprise Clinical AI \& Technology
Job Summary
The Senior Advisor, Clinical Product \& Clinical AI Enablement is a clinically trained individual contributor *(Registered Nurse, Advanced Practice Provider, or Clinical Pharmacist)* who supports the design, shaping, and execution of Clinical AI–enabled products and capabilities across The Cigna Group, including Cigna Healthcare and Evernorth. This role operates at the enterprise level, enabling Clinical AI across business units. The Senior Advisor brings deep clinical expertise and real‑world workflow understanding to help ensure that AI\-enabled and clinical technology products are clinically sound, operationally feasible, and scalable across diverse business contexts (payer, pharmacy, care delivery enablement, and services).
This is a hands‑on, tactical, execution‑focused role that serves as a trusted clinical product advisor, partnering with product, data and analytics, technology, and business teams to translate defined Clinical AI priorities into implementable solutions that drive health, experience, and value outcomes across the enterprise.
Key Responsibilities
Enterprise Clinical AI Product Enablement
- Support execution of enterprise Clinical AI initiatives spanning Cigna Healthcare and Evernorth, providing clinical input across design, pilot, deployment, and evaluation.
- Help shape AI\-enabled clinical products by translating enterprise\-defined use cases into clinical workflows, functional requirements, and success criteria.
- Participate in clinical review and validation of Clinical AI solutions to ensure appropriateness, usability, and alignment with enterprise clinical standards.
- Identify workflow risks, adoption barriers, or potential unintended consequences across varied business contexts and surface recommendations to product and technology teams.
- Support post‑implementation monitoring of Clinical AI products, focusing on clinical relevance, operational fit, and measurable outcomes.
Clinical Product Shaping \& Workflow Translation
- Provide clinical input into product requirements and measures used to demonstrate impact across health plan, pharmacy, and services organizations.
- Act as a clinical product advisor to enterprise product and technology teams, grounding product decisions in clinical knowledge and real‑world care workflows.
- Review user interface (UI) and user experience (UX) designs from a clinician perspective to support scalability, usability, and adoption across business units.
- Collaborate with enterprise stakeholders and contribute practical recommendations to optimize workflows enabled by AI and clinical technologies, minimizing friction and cognitive burden.
Clinical Data, Analytics \& Measurement Support
- Partner with data and analytics teams to support development of clinically meaningful dashboards and reporting.
- Assist in defining and validating clinical, operational, and value‑based metrics tied to enterprise Clinical AI initiatives.
- Ensure metrics and insights are understandable, actionable, and appropriate for enterprise stakeholders.
Interoperability \& Data Enablement
- Support efforts to enable clinical data interoperability across enterprise systems, ensuring Clinical AI and product workflows are powered by timely, accurate, and usable data.
- Provide clinical input into data integration use cases (e.g., ADT feeds, longitudinal records, pharmacy and claims data alignment) to support AI and clinical products and services.
- Partner with technology and data teams to ensure clinical context is incorporated into data models, data flows, and integration priorities.
- Identify gaps in data availability or usability that may impact clinical outcomes, performance, or workflow execution, and contribute recommendations for improvement.
Cross‑Enterprise Collaboration \& Execution Support
- Work closely with product managers, data scientists, engineers, clinicians, pharmacists, and business leaders in a highly matrixed enterprise environment.
- Support issue identification and resolution during implementation; escalate concerns or risks as appropriate.
- Assist in preparing clear, concise materials (briefs, slides, summaries) that communicate clinical considerations, progress, and outcomes to enterprise audiences.
What Success Looks Like in This Role
- Clinical AI products are shaped with strong clinical input and successfully enabled across Cigna Healthcare and Evernorth.
- Product and AI teams receive practical, business‑relevant clinical guidance that accelerates execution and adoption.
- Clinical AI solutions scale across business units while remaining clinically credible, usable, and trusted.
- Clinical, operational, and value‑based outcomes are clearly defined and measurable.
Required Qualifications
- Clinical Background
- Active clinical license required:
+ Registered Nurse (RN), or
+ Advanced Practice Provider (NP or PA), or
+ Clinical Pharmacist (PharmD)
- Experience practicing in a care delivery environment, with strong understanding of clinical workflows and decision‑making.
Education
- Bachelor’s degree required
- Advanced degree or training/experience in healthcare, population health, clinical informatics, analytics, or related field preferred
Experience
- 6–10 years of relevant experience in clinical practice, clinical product enablement, population health, or clinical technology implementation.
- Experience contributing clinical expertise to enterprise‑scale products or programs, ideally across payer, pharmacy, or services organizations.
- Working knowledge of Clinical AI or advanced analytics applications in healthcare, including benefits, limitations, and adoption considerations.
- Experience supporting Population Health and/or Value‑Based Care initiatives preferred.
Core Competencies
- Strong clinical judgment applied to enterprise product decisions
- Ability to shape clinical products through workflow and execution insight
- Comfort working in complex, evolving enterprise environments
- Effective collaborator across clinical, product, and technical teams
- Detail‑oriented with a bias toward execution
- Clear, concise communicator
If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.
For this position, we anticipate offering an annual salary of 116,100 \- 193,500 USD / yearly, depending on relevant factors, including experience and geographic location.
This role is also anticipated to be eligible to participate in an annual bonus plan.
At The Cigna Group, you’ll enjoy a comprehensive range of benefits, with a focus on supporting your whole health. Starting on day one of your employment, you’ll be offered several health\-related benefits including medical, vision, dental, and well\-being and behavioral health programs. We also offer 401(k), company paid life insurance, tuition reimbursement, a minimum of 18 days of paid time off per year and paid holidays. For more details on our employee benefits programs, click here.
About The Cigna Group
Doing something meaningful starts with a simple decision, a commitment to changing lives. At The Cigna Group, we’re dedicated to improving the health and vitality of those we serve. Through our divisions Cigna Healthcare and Evernorth Health Services, we are committed to enhancing the lives of our clients, customers and patients. Join us in driving growth and improving lives.*Qualified applicants will be considered without regard to race, color, age, disability, sex, childbirth (including pregnancy) or related medical conditions including but not limited to lactation, sexual orientation, gender identity or expression, veteran or military status, religion, national origin, ancestry, marital or familial status, genetic information, status with regard to public assistance, citizenship status or any other characteristic protected by applicable equal employment opportunity laws.*
*If you need a reasonable accommodation to complete the online application process, please email* *[email protected]* *for assistance. Please note that this email inbox is dedicated to accommodation requests only and cannot provide application updates or accept resumes.*
*The Cigna Group has a tobacco\-free policy and reserves the right not to hire tobacco/nicotine users in states where that is legally permissible. Candidates in such states who use tobacco/nicotine will not be considered for employment unless they enter a qualifying smoking cessation program prior to the start of their employment. These states include: Alabama, Alaska, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Iowa, Kansas, Maryland, Massachusetts, Michigan, Nebraska, Ohio, Pennsylvania, Texas, Utah, Vermont, and Washington State.*
*Qualified applicants with criminal histories will be considered for employment in a manner* *consistent with all federal, state and local ordinances.*
Salary Context
This $116K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At The Cigna Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($154K) sits 15% below the category median. Disclosed range: $116K to $193K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Cigna Group AI Hiring
The Cigna Group has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Plano, TX, US, Remote, US. Compensation range: $193K - $297K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.