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About This Role
SUMMARY:
The Product Management Senior Manager, Patient Engagement & AI will lead a team responsible for advancing customer engagement capabilities across patient communications, CRM experiences, conversational AI, and related digital engagement platforms. This role plays a critical part in shaping how patients, providers, clients, and internal stakeholders interact with our organization through personalized, seamless, and AI-enabled experiences.
The Senior Manager will partner across Business, Operations, Technology, Clinical, Legal, Compliance, Marketing, and Vendor organizations to develop engagement strategies, drive communication effectiveness, enhance customer journeys, and expand AI-powered experiences that improve satisfaction, operational efficiency, and business outcomes.
This is an opportunity to help define the future of healthcare engagement by combining communication strategy, customer relationship management, and conversational AI to create meaningful and scalable experiences across the customer lifecycle.
RESPONSIBILITIES:
Lead Patient Engagement Strategy
- Lead and develop a team responsible for patient engagement initiatives, communication strategies, CRM capabilities, and AI-powered customer experiences.
- Define and execute engagement strategies that improve customer satisfaction, increase adoption of digital capabilities, and support business objectives.
- Partner with stakeholders to develop communication approaches that deliver the right message, through the right channel, at the right time.
- Identify opportunities to improve customer journeys and reduce friction across engagement touchpoints.
- Establish success metrics and outcome-based measurements to evaluate engagement effectiveness and business impact.
Drive Customer Communications and CRM Excellence
- Lead the evolution of customer communication capabilities across digital and operational channels.
- Partner with business stakeholders to develop communication strategies that support awareness, education, engagement, and behavior change.
- Drive CRM strategy, optimization, and roadmap planning to improve personalization, segmentation, campaign effectiveness, and customer outreach.
- Support the development of customer journey orchestration capabilities that create consistent and connected experiences across channels.
- Ensure communications align with customer needs, business goals, compliance requirements, and brand standards.
Advance AI-Powered Experiences
- Lead strategic initiatives related to conversational AI and virtual assistant capabilities, including AI-driven IVR and future AI-enabled customer engagement solutions.
- Identify opportunities to leverage artificial intelligence and automation to improve customer experiences and operational efficiency.
- Partner with technology teams and vendors to evaluate emerging capabilities and define future-state engagement strategies.
- Support innovation efforts that integrate AI within communication channels, customer journeys, and service experiences.
- Drive adoption and continuous improvement of AI-powered solutions through data, customer feedback, and performance insights.
Orchestrate Cross-Functional Initiatives
- Serve as the workstream leader for complex programs requiring coordination across Business, Product, Operations, Technology, Analytics, Legal, Compliance, Marketing, and Vendor organizations.
- Build alignment across stakeholders with competing priorities and diverse business objectives.
- Manage dependencies, risks, communications, governance activities, and execution plans to ensure successful delivery.
- Facilitate strategic planning sessions, working groups, and executive governance forums.
- Drive accountability and progress across teams without direct authority.
Influence Enterprise Strategy and Business Outcomes
- Develop executive-level communications, business cases, recommendations, presentations, and strategic planning materials.
- Partner with senior leaders to define future engagement strategies and identify opportunities for innovation, modernization, and operational improvement.
- Utilize customer insights, engagement metrics, operational performance data, and market trends to inform prioritization and decision-making.
- Support annual planning, investment decisions, portfolio prioritization, and organizational goal setting.
- Foster a culture of innovation, accountability, continuous improvement, and customer-centric thinking.
Build Organizational Capability
- Mentor and develop product management and engagement strategy talent.
- Promote best practices in customer engagement, communication planning, CRM, AI adoption, and journey orchestration.
- Establish governance processes, operating models, and measurement frameworks that support long-term scalability and success.
- Build strong partnerships across the organization to ensure alignment and execution excellence.
IDEAL CANDIDATES WILL HAVE A COMBINATION OF THE FOLLOWING:
- 5+ years of experience in product management, customer engagement, CRM, communications strategy, digital transformation, healthcare technology, or related disciplines.
- Experience leading people, teams, programs, or large-scale cross-functional initiatives.
- Experience developing business strategies, communication plans, customer engagement programs, or CRM-driven initiatives.
- Demonstrated success leading complex initiatives involving multiple stakeholder groups and business functions.
- Strong analytical, organizational, problem-solving, and strategic thinking skills.
- Ability to influence decisions and drive alignment without direct authority.
- Exceptional verbal, written, and presentation skills.
- Experience using customer insights and data to inform strategy and decision-making.
- Demonstrated ability to navigate ambiguity and drive results in a dynamic environment.
- Bachelor's degree in Business, Communications, Healthcare Administration, Information Systems, Engineering, or a related field.
- Experience supporting healthcare, specialty pharmacy, pharmacy benefit management (PBM), or health services organizations.
- Experience with CRM platforms, customer journey management, campaign management, or engagement technologies.
- Experience with conversational AI, generative AI, virtual assistants, digital engagement platforms, or contact center technologies.
- Experience with Salesforce, Amazon Connect, Microsoft technologies, Genesys, or similar enterprise platforms.
- Experience leading customer experience, communications, AI, or digital transformation initiatives.
- Experience presenting strategies, business cases, and recommendations to senior leadership.
- Product Management, Agile, AI, Marketing, CRM, or Project Management certifications.
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 131,400 - 219,000 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, paid holidays, and leaves of absence. For more details on our employee benefits programs, click here.
About Evernorth Health Services
Evernorth Health Services, a division of The Cigna Group, creates pharmacy, care and benefit solutions to improve health and increase vitality. We relentlessly innovate to make the prediction, prevention and treatment of illness and disease more accessible to millions of people. 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 $131K-$219K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $131K to $219K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
The Cigna Group AI Hiring
The Cigna Group has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Bloomfield, CT, US, Orlando, FL, US, Morris Plains, NJ, US. Compensation range: $89K - $219K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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