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Position Purpose: Leads Centene's AI innovation and strategic technology partnerships function, providing enterprise technical leadership for the highest-value AI use cases and shaping partner platforms/harnesses evolve to meet Centene's needs. Works directly with business, data, engineering, security, and governance leaders to manage complex opportunities where AI can materially improve outcomes, redesign workflows, or create new enterprise capabilities. Accountable for translating those opportunities into executable technical strategies, reference architectures, prototypes, and scaled solutions that combine Centene data and context with foundation models, agents, tools, and vendor platforms. Serves as the senior technical counterpart to business partners, influencing product roadmaps, defining joint development priorities, and ensuring vendor harnesses remain interoperable, extensible, observable, and aligned to Centene's AI control plane and enterprise architecture. Advises executive leadership on emerging AI capabilities, technical tradeoffs, partnership strategy, and where Centene should build, buy, partner, or co-develop.
AI Innovation and High-Value Use Case Leadership
- Sets the enterprise agenda for data context and AI innovation, focusing resources on high-value use cases where advanced AI can materially improve member outcomes, operating performance, or medical cost management.
- Partners with executive business and technology leaders to frame complex problems, test whether AI is the right solution, and translate priority opportunities into clear technical hypotheses, target workflows, and measurable outcomes.
- Leads multidisciplinary teams through discovery, rapid prototyping, technical validation, and production architecture, balancing speed of learning with security, reliability, governance, and long-term maintainability.
Vendor Harness Strategy and Partnerships
- Owns the technical strategy for Centene's use of vendor AI harnesses, defining the capabilities these platforms must provide and how they should integrate with Centene's data, context, identity, security, model, and observability services.
- Serves as the senior technical counterpart to strategic AI vendors, shaping joint roadmaps, co-development priorities, platform extensions, and commitments required to support Centene's highest-value use cases.
- Evaluates emerging vendor capabilities through technical diligence, architecture reviews, prototypes, and competitive testing, separating demonstrated capability from product claims and recommending where Centene should build, buy, partner, or co-develop.
- Drives the evolution of vendor harnesses toward open, composable, and portable patterns, including support for multiple models, reusable agents and tools, enterprise context, standard protocols and APIs, evaluation, tracing, cost attribution, and lifecycle controls.
- Partners with Procurement, Legal, Security, Enterprise Architecture, and AI Governance to ensure strategic agreements support technical interoperability, responsible AI, data protection, auditability, roadmap influence, and protection of Centene intellectual property and context.
Architecture, Engineering, and Scaled Adoption
- Defines how priority AI solutions use Centene's AI control plane, agentic framework, AI Gateway, enterprise context layer, trusted data products, and existing engineering platforms.
- Establishes technical quality standards for prototypes and production solutions, including evaluation, testing, security, reliability, observability, model and prompt change management, and operational ownership.
- Works with platform and engineering leaders to identify gaps exposed by priority use cases and convert those needs into platform roadmap priorities.
- Ensures innovation efforts transition into durable products with clear ownership, support models, adoption plans, performance measures, and value tracking rather than remaining disconnected pilots.
- Maintains a disciplined view of the external AI market, identifies capabilities with strategic relevance to Centene, and runs focused experiments to determine when emerging technology is ready for enterprise use.
Executive and Team Leadership
- Advises senior leadership on AI technology trends, architecture choices, vendor dependencies, investment priorities, and the practical implications of rapidly changing model and platform capabilities.
- Represents Centene in strategic technology partnerships and external forums, strengthening relationships with leading AI companies, startups, research organizations, and industry partners while bringing relevant learning back into the enterprise.
- Performs other duties as assigned.
- Comply with all policies and standards.
Education/Experience:
- Requires a Bachelor's degree,12+ years of related experience, including significant leadership experience in artificial intelligence, machine learning, enterprise architecture, technology strategy, product innovation, software engineering, cloud platforms, data and analytics, or a related discipline.
- Or equivalent experience acquired through accomplishments of applicable knowledge, duties, scope and skill reflective of the level of this position.
- 10+ years experience leading enterprise technology initiatives, AI/ML solutions, software engineering, cloud platforms, data and analytics, enterprise architecture, or technology strategy, including executive-level leadership responsibilities required.
- Demonstrated experience translating complex business opportunities into scalable technical solutions through AI, automation, advanced analytics, and emerging technologies preferred.
- Experience working within healthcare, managed care, or other highly regulated industries, including implementation of secure, compliant, and scalable AI solution preferred.
Pay Range: $207,000.00 - $392,100.00 per year At Centene, we connect people to the care they need to live healthier lives — and the work you do here makes that impact real every day. You’ll take on meaningful challenges that directly support individuals, families, and communities, building your skills while making healthcare more accessible and effective. It’s work with a purpose you can see, backed by a team committed to improving lives well beyond the workday.
Centene offers a comprehensive benefits package including: competitive pay, health insurance, 401K and stock purchase plans, tuition reimbursement, paid time off plus holidays, and a flexible approach to work with remote, hybrid, field or office work schedules. Actual pay will be adjusted based on an individual's skills, experience, education, and other job-related factors permitted by law, including full-time or part-time status. Total compensation may also include additional forms of incentives. Benefits may be subject to program eligibility.
Centene is an equal opportunity employer that is committed to diversity, and values the ways in which we are different. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other characteristic protected by applicable law.
Qualified applicants with arrest or conviction records will be considered in accordance with the LA County Ordinance and the California Fair Chance Act
Salary Context
This $207K-$392K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Centene, 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($299K) sits 41% above the category median. Disclosed range: $207K to $392K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Centene AI Hiring
Centene has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in MO, US. Compensation range: $326K - $392K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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