Interested in this AI/ML Engineer role at The Cigna Group?
Apply Now →Skills & Technologies
About This Role
Principal Architect \- PBM Modernization \& AI Engineering
The Principal Architect \- PBM Modernization \& AI Engineering is a senior architecture leader responsible for designing and delivering modern technology solutions that accelerate the transformation of the Pharmacy Benefit Management (PBM) ecosystem.
As a key member of the PBM modernization program, this individual will partner closely with product, engineering, data, and business teams to modernize legacy platforms and enable scalable, cloud\-native, API\-first, and event\-driven architectures. The Principal Architect will bring deep technical expertise developed through a progression from software engineering into architecture leadership and will serve as a trusted advisor on application architecture, engineering excellence, technology modernization, and AI adoption.
The successful candidate will possess extensive experience designing, building, and modernizing enterprise platforms utilizing modern technology stacks, including React, Java, Go, Node.js, cloud\-native services, and modern data architectures. This role requires expertise across application architecture, data engineering, integration strategies, distributed systems, and software delivery practices, with the ability to guide engineering teams through complex transformation initiatives.
Artificial Intelligence will be a critical component of this role. The Principal Architect will help define and implement architectures that leverage Generative AI, Agentic AI, and intelligent automation to streamline business processes, improve operational efficiency, enhance customer experiences, and unlock new business capabilities. This leader will champion practical AI adoption across the software development lifecycle while ensuring solutions remain scalable, secure, compliant, and aligned with enterprise standards.
Key Responsibilities
Modernization, AI \& Engineering Leadership
- Lead the design and implementation of architecture solutions supporting strategic PBM modernization initiatives.
- Drive the transformation of legacy applications into modular, cloud\-native, API\-first, and event\-driven platforms.
- Establish architecture standards, reference patterns, and engineering best practices that improve scalability, resiliency, maintainability, and speed to market.
- Provide hands\-on technical leadership through architecture reviews, solution design, proof\-of\-concept development, and critical engineering initiatives.
- Partner with engineering teams to modernize application platforms using React, Java, Go, Node.js, Kubernetes, cloud\-native services, and modern integration patterns.
- Define and implement data architecture, data modeling, and data engineering strategies that support operational, analytical, and AI\-driven business capabilities.
- Drive adoption of DevSecOps, platform engineering, architecture automation, and AI\-assisted software development practices.
- Identify and implement opportunities to embed Generative AI, Agentic AI, and intelligent automation capabilities within PBM products and business workflows.
- Define architectural patterns that support AI\-enabled workflow automation, intelligent decision support, knowledge management, and operational excellence.
- Leverage AI\-powered engineering platforms and development copilots, including Claude Code, Cursor, GitHub Copilot, and emerging technologies, to accelerate modernization efforts, improve developer productivity, enhance software quality, and reduce delivery timelines.
- Design and implement AI\-enabled software engineering capabilities, including SDLC agents, reusable skills, prompt frameworks, and specification\-driven development practices that automate architecture, coding, testing, documentation, and modernization workflows.
- Ensure all solutions are secure, scalable, observable, compliant, and aligned with enterprise architecture principles and governance standards.
- Mentor architects and engineers while fostering a culture of technical excellence, innovation, continuous learning, and modernization.
Preferred Qualifications
- 8\+ years of experience in software engineering, solution architecture, and technology leadership.
- Demonstrated progression from hands\-on software engineering roles into architecture leadership positions.
- Deep expertise designing and building cloud\-native applications utilizing Java, Go, Node.js, React, APIs, microservices, and event\-driven architectures.
- Strong background in data architecture, data modeling, data engineering, and modern data platforms.
- Experience implementing Generative AI, Agentic AI, large language model (LLM)\-based solutions, and intelligent automation capabilities in enterprise environments.
- Experience within healthcare, pharmacy benefit management (PBM), payer, or pharmacy technology domains preferred.
- Proven ability to influence senior technology and business leaders while driving large\-scale modernization initiatives.
- Strong communication, collaboration, and stakeholder management skills with the ability to translate complex technical concepts into business value.
- Bachelor degree in Computer Science or related fields
Why This Role Matters
The Principal Architect serves as a key technical leader in the PBM modernization journey, translating strategic architecture vision into practical, scalable solutions. This role will help reduce technical debt, accelerate innovation, enable AI\-driven transformation, and establish the technology foundation for the next generation of PBM platforms and digital capabilities. Through a combination of architectural leadership, engineering excellence, and AI innovation, this individual will play a critical role in shaping the future of the PBM technology landscape.
This version elevates the role to a more executive\-level, strategic Principal Architect position while maintaining the hands\-on modernization and AI engineering expectations.
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.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.*
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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.