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About This Role
Job Description: Healthcare Claims AI / Technical Product Leader to own the vision, strategy, and delivery of AI\-powered products and solutions across the healthcare claims lifecycle. This role sits at the intersection of product strategy, AI engineering leadership, and healthcare claims domain expertise—spanning claims adjudication, claims adjustment, payment integrity (PI), coordination of benefits (COB), subrogation, provider dispute resolution, and fraud/waste/abuse (FWA) detection.
You will lead cross\-functional teams to architect, build, and operationalize Agentic AI and Generative AI solutions that create measurable value—reducing claims processing costs, accelerating adjudication turnaround, improving payment accuracy, strengthening audit outcomes, and modernizing provider operations workflows. This is a high\-visibility leadership role ideal for someone passionate about combining technology, healthcare claims depth, and design thinking to create lasting business impact for national payers.
Responsibilities: AI Product Strategy \& Roadmap Ownership
- Define, own, and evolve the AI product roadmap for healthcare claims solutions—spanning claims adjudication automation, claims adjustment, payment integrity, COB/subrogation, provider issue resolution, and FWA detection.
- Translate complex claims operational challenges into AI\-first product strategies with clear business cases, ROI frameworks, and measurable KPIs (e.g., auto\-adjudication rate, denial accuracy, overpayment recovery yield).
Drive solutions from ideation POC MVP* production scale, using agile execution and business\-centric prioritization.
- Maintain a competitive landscape matrix and continuously benchmark against market players (Optum/Change Healthcare, Cotiviti, Cognizant TriZetto, HealthEdge, Conduent, etc.) to inform differentiation strategy.
- Partner with sales, finance, and leadership to determine pricing, packaging, and go\-to\-market approach (managed services, SaaS, outcome\-based/gainshare models).
2\. Technical \& AI Engineering Leadership
- Drive the transition from traditional rules\-engine claims processing to AI\-augmented adjudication—automating claim edits, benefit configuration interpretation, provider contract parsing, and payment rule application.
- Design reusable AI components and platform capabilities (e.g., claims document intelligence, EOB/remittance parsing, provider contract extraction, coding validation engines, browser/desktop automation agents).
- Write clear product requirements documents (PRDs), user stories, and technical specifications with well\-defined acceptance criteria for engineering teams.
- Champion explainability\-first AI design, ensuring all models produce audit\-ready, evidence\-grounded outputs suitable for SIU investigations, CMS audits, and payer compliance reviews.
3\. Healthcare Claims Domain \& Operational Transformation
- Apply deep knowledge of end\-to\-end claims operations—claim intake, edits, adjudication, pricing, payment, adjustment, appeals, grievances, provider disputes, and overpayment recovery—to identify high\-impact AI use cases.
- Embed AI solutions into core claims platforms and systems (QNXT, Facets, Amisys, HealthRules Payer, CSC/DXC, etc.) through platform\-agnostic integration and API\-first design.
- Design AI\-driven solutions covering pre\-pay and post\-pay analytics, DRG validation, code editing (CPT/ICD\-10/HCPCS), duplicate claim detection, and provider billing pattern analysis.
- Build cross\-payer intelligence capabilities that leverage anonymized, aggregated claims data to drive payment accuracy benchmarks, denial pattern optimization, and cost\-of\-care insights.
- Support RFP/RFI responses, orals preparation, and executive presentations for national payer pursuits.
- Develop AI\-powered provider operations solutions—automated provider issue resolution, correspondence generation, contract interpretation, and fee schedule management.
4\. Client Engagement \& Thought Leadership
- Serve as the product spokesperson and AI SME in client engagements—leading executive presentations, POC demonstrations, workshops, and roadmap discussions with CXO/EVP/VP stakeholders at national payers.
- Partner with account management and business development teams to shape differentiated claims AI solutions for complex, enterprise\-level healthcare pursuits.
- Build and deliver compelling thought leadership content—white papers, case studies, conference presentations, and analyst briefings—that position EXL as a leader in claims AI innovation.
- Represent EXL at industry conferences (AHIP, HCCA, SIU conferences, RISE, etc.) through presentations, panel discussions, and live product demonstrations.
5\. Team Leadership \& Talent Development
- Lead, mentor, and develop a high\-performing team of AI engineers, product managers, data scientists, and solution architects focused on claims AI.
- Foster a culture of innovation, collaboration, accountability, and continuous learning within the team.
- Collaborate with offshore engineering and delivery teams to ensure timely, secure, and scalable implementation.
- Build and scale an AI Center of Excellence for healthcare claims, establishing reusable agent frameworks, governance playbooks, and best practices.
6\. Governance, Compliance \& Risk Management
- Champion privacy\-first design, data anonymization, and compliance with HIPAA, PHI/PII handling, and payer\-specific data governance frameworks.
- Ensure all AI solutions meet scalability, security, auditability, and operational excellence requirements for regulated healthcare claims environments.
- Establish governance\-grade AI controls including model monitoring, bias detection, drift management, and human\-in\-the\-loop override mechanisms for claims decisioning.
- Maintain compliance with CMS, state DOI regulations, NAIC guidelines, and payer\-specific audit requirements for claims processing and payment integrity.
Qualifications: Experience
- 15\+ years of progressive experience in AI/ML engineering, technical product management, or platform product leadership roles.
- 5\+ years of leadership experience in healthcare claims technology, payer claims operations, or health\-tech product organizations focused on claims/PI.
- Proven track record of building and scaling AI solutions for claims processing, payment integrity, or provider operations—from POC to production at an enterprise scale.
- Experience with large\-scale payer claims engagements ($25M\+ in managed services or technology contracts) and familiarity with FTE\-to\-AI transformation models in claims shops.
- Bachelor's degree in computer science, Engineering, Data Science, or related technical field. Master's degree (M.Tech / MS / MBA) is strongly preferred.
Technical Skills
- Hands\-on and architectural expertise in LLMs, embeddings, vector search, prompt engineering, and RAG pipelines.
- Proficiency with cloud AI platforms: Azure OpenAI, AWS Bedrock (Claude, Sonnet), GCP Vertex AI.
- Experience with agent orchestration frameworks: LangChain, LangGraph, CrewAI, AutoGen, or equivalent Agentic AI frameworks.
- Strong understanding of MCP (Model Context Protocol), A2A protocols, and multi\-agent system design.
- Familiarity with browser/desktop automation tools (Playwright, Selenium) as AI agent execution layers for legacy claims system navigation.
- Familiarity with secure API design, OAuth2/JWT, enterprise integration patterns, and EDI transaction sets (X12 837/835/270/271/276/277\).
Domain Skills
- Deep understanding of end\-to\-end healthcare claims operations: claim submission, edits, adjudication, pricing, payment, adjustment, appeals, grievances, and overpayment recovery.
- Strong knowledge of pre\-pay/post\-pay review, DRG validation, CPT/ICD\-10/HCPCS code editing, COB, subrogation, and FWA detection methodologies.
- Experience with claims processing platforms and systems (QNXT, Facets, Amisys, HealthRules Payer, CSC/DXC) and provider data management.
- Understanding of healthcare financial models: PMPM, total cost of care, provider reimbursement methodologies (fee\-for\-service, value\-based, capitation), and gainshare/outcome\-based pricing structures.
- Familiarity with CMS regulations, state DOI requirements, NAIC model acts, and payer audit/compliance frameworks relevant to claims processing.
Leadership \& Communication
- Exceptional executive communication skills—ability to present AI strategy and business impact to C\-suite audiences (CEO, President, COO) at national payer organizations.
- Proven ability to influence cross\-functional stakeholders across engineering, delivery, sales, and claims operations teams.
- Experience leading distributed, global teams (US \+ offshore) in fast\-paced, high\-growth environments.
- Strong storytelling ability—translating complex AI capabilities into clear business value, ROI narratives, and claims transformation roadmaps.
Preferred Qualifications
- Experience building or leading AI Centers of Excellence or reusable agent frameworks specifically for claims processing or payment integrity.
- Patent holder, published researcher, or recognized speaker in the AI/healthcare claims innovation space.
- Experience with document intelligence and IDP platforms for claims\-related documents (EOBs, remittance advice, provider contracts, fee schedules).
- Deep familiarity with healthcare data standards: HL7, FHIR, X12 EDI (837/835/270/271/276/277\), NCPDP for pharmacy claims.
- Experience with Databricks, Snowflake, or similar data platforms in healthcare claims analytics context.
- Background in consulting or managed services delivery for large payer claims operations.
- Knowledge of competitive landscape including Optum/Change Healthcare, Cotiviti, Cognizant TriZetto, HealthEdge, Conduent, Gainwell Technologies, and emerging AI\-native claims startups.
- Certified Professional Coder (CPC), Accredited Healthcare Fraud Investigator (AHFI), or similar claims/PI certifications a plus.
- Experience with claims processing platforms and systems (QNXT, Facets, Amisys, HealthRules Payer, CSC/DXC) and provider data management.
- Understanding of healthcare financial models: PMPM, total cost of care, provider reimbursement methodologies (fee\-for\-service, value\-based, capitation), and gainshare/outcome\-based pricing structures.
- Familiarity with CMS regulations, state DOI requirements, NAIC model acts, and payer audit/compliance frameworks relevant to claims processing.
Leadership \& Communication
- Exceptional executive communication skills—ability to present AI strategy and business impact to C\-suite audiences (CEO, President, COO) at national payer organizations.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EXL Service, 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 $181,170 based on 12,692 positions with disclosed compensation.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EXL Service AI Hiring
EXL Service has 9 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Architect. Positions span US, Jersey City, NJ, US. Compensation range: $150K - $280K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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