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About This Role
Position Purpose: Responsible for building and governing the enterprise knowledge foundation that powers AI-generated insights. This role oversees the capture, curation, structure, and lifecycle management of business knowledge, ensuring organizational expertise, business processes, policies, rules, and contextual information are transformed into trusted, AI-consumable assets. The leader is accountable for enterprise knowledge strategy and execution, data governance, metadata strategy, and content that powers the AI context layer. The leader will serve as a strategic bridge between business teams, analytics organizations, and engineering partners to ensure enterprise knowledge is accurate, current, discoverable, and aligned with authoritative sources of truth. The leader will drive strategic advantage from enterprise knowledge assets to position the organization at the forefront of AI-enabled decision-making and agentic production.
AI Content Development and Strategic Execution
- Own Enterprise Knowledge Strategy and AI Content Management, balancing strategic vision with operational execution to transform institutional knowledge into a strategic asset that creates competitive advantage and accelerates AI adoption.
- Identify, document, and transform critical institutional knowledge into scalable information assets, developing and maintaining domain-specific knowledge bases that include business context, taxonomies, knowledge-to-data mappings, and metric standards.
- Establish an information lifecycle management framework for knowledge assets designed to intake, curate, validate, evolve, and retire content while preserving integrity and minimizing bias through drift prevention, relevance management, and continuous validation.
- Partner with technology teams to map institutional knowledge and information assets to trusted data sources that power generative and agentic AI solutions
- Possess AI literacy and apply conceptual knowledge of retrieval-augmented generation (RAG), vector databases, and content optimization techniques to structure information assets for retrieval accuracy and AI effectiveness.
Influence, Collaboration, and Stakeholder Management
- Collaborate cross-functionally with data engineering, analytics, and business leadership to ensure alignment and prioritization of AI content knowledge creation and data governance efforts.
- Discover and translate AI business needs and use cases into explicit knowledge requirements by domain, and partner to gather and document key information into reusable AI context assets
- Communicate on progress, barriers, opportunities, and outcomes surrounding knowledge strategy and governance policies to senior leadership and other key stakeholders.
Embedded Data Governance
- Maintain a robust data governance framework to ensure data accuracy, consistency, and security in all Data Products, Analytics Products, and AI insights.
- Enable data users by maintaining a dynamic data and metrics catalogue, providing enablement and training opportunities, and offering issue support and resolution.
- Partner with data engineering to close data quality gaps and embed governance early in data layer creation.
- Foster a culture of data stewardship and accountability throughout the organization.
- Ensure data and knowledge assets are leveraged effectively to enable Centene's business and AI goals.
- Develop and enforce data ownership policies, usage standards, and best practices across the organization.
Team Leadership and Development
- Lead, grow, and develop a high-performing team of data governance specialists and data analysts.
- Guide and align individual and team level goals to organizational strategic direction.
- Provide mentorship and professional development opportunities to team members.
- Promote a culture of continuous improvement and innovation across the data organization and in the business units.
- Performs other duties as assigned.
- Comply with all policies and standards.
Education/Experience:
- Bachelor's degree required.
- 10+ years of related experience, including prior management experience.
- Prior healthcare and/or managed care industry experience strongly preferred.
- Or equivalent experience acquired through accomplishments of applicable knowledge, duties, scope and skill reflective of the level of this position.
Pay Range: $171,900.00 - $326,900.00 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 $171K-$326K 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 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($249K) sits 18% above the category median. Disclosed range: $171K to $326K.
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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