Interested in this AI/ML Engineer role at The Cigna Group?
Apply Now →About This Role
The Director, Technical Product Management \- Applied AI leads the strategy, development, and delivery of AI\-enabled products and technology solutions that improve Finance operations and decision\-making. Working across Finance, Engineering, Data, and Architecture teams, this leader translates complex business needs into practical product strategies, prioritized roadmaps, and scalable enterprise solutions.
The ideal candidate combines strong technical product leadership with a working knowledge of modern AI, data platforms, and enterprise architecture. This individual thrives in a global, matrixed environment, and drives cross\-functional teams from concept through adoption and measurable business impact.
Key Responsibilities
Product Strategy and Portfolio Leadership
- Define and execute the product vision, strategy, and roadmap for Finance AI products and enabling technology capabilities.
- Partner with Finance process owners and technical leaders to identify high\-value opportunities, clarify user needs, and convert business problems into prioritized product requirements.
- Make product decisions that balance business value, user experience, technical feasibility, risk, and long\-term maintainability.
- Promote reusable capabilities and scalable solution patterns that can be applied across Finance rather than creating isolated point solutions.
Technical Product Delivery
- Lead cross\-functional delivery across discovery, solution design, development, testing, deployment, and post\-launch optimization.
- Coordinate complex technical dependencies, integrations, and sequencing across Engineering, Data, Architecture, and other enterprise partners.
- Ensure products meet enterprise expectations for scalability, reliability, supportability, security, data integrity, and architectural alignment.
- Establish clear outcomes, milestones, and decision points; surface risks early and drive timely resolution of delivery barriers.
Stakeholder Partnership and Adoption
- Build trusted partnerships with Finance process owners, technology teams, and program leaders to align product delivery with strategic priorities and desired business outcomes.
- Communicate progress, risks, dependencies, and tradeoffs clearly to executive sponsors and steering forums, enabling informed decisions.
- Drive readiness, adoption, and operationalization of delivered capabilities.
- Influence across a global matrixed organization, aligning stakeholders with different priorities around a shared product direction.
Required Qualifications
- 8\+ years of experience building, managing, or delivering enterprise platforms or technology products, including significant technical product management responsibilities.
- Demonstrated experience partnering with Engineering and Architecture teams to deliver complex, integrated solutions.
- Strong understanding of enterprise technology, data platforms, and AI capabilities, with the ability to evaluate practical use cases, constraints, and tradeoffs.
- Ability to translate business problems into clear product requirements, roadmaps, and executable delivery plans.
- Experience leading initiatives in a global, matrixed organization and influencing stakeholders.
- Executive\-level communication skills, including the ability to present recommendations, delivery risks, and tradeoffs to business and technical leaders.
- Demonstrated ability to navigate ambiguity, manage competing priorities, and drive results in an evolving environment.
- Bachelor's degree in Computer Science, Information Systems, or Business Administration or related field.
Preferred Qualifications
- Experience delivering AI\-enabled products or data\-intensive solutions
- Finance or healthcare industry experience.
- Experience supporting product adoption, operating model changes, or enterprise\-scale transformation.
- Master's degree in Computer Science, Information Systems, or Business Administration or related field.
- Locations: Hybrid work arrangement in Bloomfield, CT, Philadelphia, PA, Morris Plains, NJ, St Louis, MO. Open to remote if not aligned to office location with a strong preference East Coast.
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 155,300 \- 258,800 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 and long term incentive 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, paid holidays, and leaves of absence. 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 $155K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $155K to $258K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The Cigna Group AI Hiring
The Cigna Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Morris Plains, NJ, US, Austin, TX, US. Compensation range: $218K - $258K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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