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About This Role
Job Summary:
The Senior Manager, AI and Data Science provides leadership for a multidisciplinary data science team that builds advanced analytics solutions using emerging Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance healthcare operations, patient care, and overall health plan performance and efficiency. This role provides strategic direction, technical oversight, and people leadership for multiple workstreams ranging from traditional predictive modeling to Agentic AI, Large Language Models (LLMs), and Natural Language Processing (NLP) solutions. The leader will also be a key driver of enterprise AI adoption by enabling the team to use Agentic AI frameworks to automate and accelerate day\-to\-day analytical and engineering tasks while ensuring appropriate governance, privacy, and responsible AI practices.
Essential Functions:* Lead, coach, and develop a data science and AI team, including hiring, goal setting, performance development, and fostering a culture of quality, learning, and responsible innovation.
- Stay current on the latest trends in LLMs, NLP, generative AI, and healthcare informatics, integrating relevant advancements into projects to drive continuous innovation.
- Develop and implement predictive models, ML algorithms, and statistical techniques identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care from large and complex healthcare datasets.
- Oversee development of predictive models using claims, EHR/EMR, lab, utilization management, and other data sources; ensure appropriate evaluation, calibration, drift monitoring, and clinical interpretability.
- Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations.
- Develop, test, and deploy generative AI solutions (including prompt engineering and RAG) with appropriate documentation, monitoring, and safeguards.
- Drive team adoption of Agentic AI frameworks to accelerate day\-to\-day work (e.g., requirements drafting, code generation, test creation, data quality checks, documentation, and analysis automation), while setting standards for safe tool use and human\-in\-the\-loop review.
- Manage a technical sub\-team focused on NLP and deep learning techniques that process and analyze unstructured healthcare data (e.g., clinical notes, patient feedback, and medical literature) to extract meaningful insights.
- Partner with architecture and data solutions teams to move solutions from prototype to production; co\-own operational readiness including documentation, observability, incident response, and post\-release tuning for both ML and GenAI systems.
- Implement LLMOps/MLOps practices: reproducible pipelines, experiment tracking, model and prompt versioning, automated evaluation, and monitoring/alerting aligned with business and clinical risk.
- Ensure compliance with HIPAA/PHI handling and internal governance standards; collaborate with privacy, security, and compliance partners to mitigate risks such as data leakage, prompt injection, and unsafe outputs.
- Collaborate with cross\-functional stakeholders (clinical leadership, risk adjustment, care management, operations, IT, and analytics partners) to prioritize work, define KPIs, and communicate results, tradeoffs, and roadmap progress.
- Represent the department in forums that require clinical AI and generative AI expertise; communicate complex concepts clearly to technical and non\-technical audiences.
- Perform any other job related duties as requested.
Education and Experience:* Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
- Master's degree or PhD preferred
- Equivalent years of relevant work experience may be accepted in lieu of required education
- Six (6\) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations required
- Three (3\) years of leadership experience required
- One (1\) year of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflakes) required
- One (1\) year of experience delivering LLM and/or generative AI solutions (e.g., prompt engineering, fine\-tuning approaches, and/or RAG) from prototype through production required
Competencies, Knowledge and Skills:* People leadership and organizational skills, including the ability to hire, coach, and retain high\-performing technical talent; set clear priorities; and build an inclusive, collaborative team culture
- Strong expertise in statistical modeling, machine learning, and predictive analytics using Python and/or R
- Familiarity with agentic AI, LLM architectures, prompt engineering, RAG, and evaluation metrics
- Familiarity with transformer architectures and modern deep learning frameworks; familiarity with generative modeling concepts and tradeoffs (quality, latency, and cost)
- Knowledge of RAG design patterns, including document processing, chunking strategies, embeddings, retrieval (keyword/semantic/hybrid), reranking, and context management
- Knowledge of evaluation methods for LLM/RAG systems (retrieval metrics, groundedness/faithfulness, answer quality, and bias/safety checks) and the ability to operationalize automated evaluation and regression testing
- Familiarity with MLOps/LLMOps practices, including CI/CD, experiment tracking, model and prompt versioning, automated testing/evaluations, monitoring/observability, and reproducible pipelines
- Expertise in Optical Character Recognition (OCR) technologies, including data extraction from scanned documents, forms, and invoices, and proficiency in OCR tools and libraries
- Understanding of AI governance and responsible AI, including privacy\-by\-design, HIPAA/PHI handling, model risk management, and safeguards against prompt injection and data leakage
- Proficiency in SQL and working knowledge of data modeling, data quality, and feature engineering for large healthcare datasets
- Knowledge of healthcare operations, payer and provider models, and industry trends
- Proficient in feature engineering techniques and exploratory data analysis
- Excellent analytical, problem\-solving, and critical\-thinking skills, including the ability to translate complex data into actionable insights
- Strong project/program leadership skills, including the ability to lead multiple initiatives simultaneously, manage dependencies, and deliver measurable outcomes
- Excellent written and verbal communication and presentation skills, including the ability to convey technical concepts to non\-technical stakeholders
- Knowledge of health care coding including CPT\-4, HCPCS, ICD\-9/10, DRG and Revenue Codes required
- Knowledge of managed care required
- Comfortable reading academic research papers and applying them in the models
Licensure and Certification:* None
Working Conditions:* General office environment; may be required to sit or stand for extended periods of time
- Ability to travel as required by the needs of the business.
Compensation Range:
$113,000\.00 \- $197,700\.00
CareSource takes into consideration a combination of a candidate’s education, training, and experience as well as the position’s scope and complexity, the discretion and latitude required for the role, and other external and internal data when establishing a salary level. In addition to base compensation, you may qualify for a bonus tied to company and individual performance. We are highly invested in every employee’s total well\-being and offer a substantial and comprehensive total rewards package.
Compensation Type (hourly/salary):
SalaryOrganization Level Competencies
- Fostering a Collaborative Workplace Culture
- Cultivate Partnerships
- Develop Self and Others
- Drive Execution
- Influence Others
- Pursue Personal Excellence
- Understand the Business
This job description is not all inclusive. CareSource reserves the right to amend this job description at any time. CareSource is an Equal Opportunity Employer. We are dedicated to fostering an environment of belonging that welcomes and supports individuals of all backgrounds.
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Salary Context
This $113K-$197K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At CareSource, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 13% below the category median. Disclosed range: $113K to $197K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
CareSource AI Hiring
CareSource has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $197K - $237K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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