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Job Description: VP, Applied AI Growth – Healthcare Markets is a senior strategic role accountable for driving revenue growth and CXO‑level expansion for EXL Healthcare by shaping and selling transformational, AI‑ and analytics‑led industry solutions. Reporting to the SVP Healthcare, this leader will design differentiated, technology‑enabled offerings that address complex payer and provider challenges across cost, quality, access, and experience. Lead specific divisions within the organization and ensure alignment with overall company goals.
The role partners closely with cross‑functional teams across analytics, digital, operations, and account leadership to incubate new solution concepts, develop rapid prototypes, and accelerate proactive deal constructs that originate outside traditional RFP cycles. As a senior client‑facing executive, the VP will extend EXL’s strong relationships to CIO/CDO and other CXOs, articulating clear value propositions, cocreating multi\-year transformation roadmaps, and building long‑term partnerships. The position blends revenue ownership, account growth, industry solutions, and hands‑on transformation consulting to deliver measurable impact on financial performance, operational efficiency, and member/patient experience.
Responsibilities: Key Responsibilities
1\. Revenue \& Growth
- Own a defined growth target for a portfolio of Transformation driven EXL Health solutions (e.g., digital clinical operations, omni‑channel engagement, payment integrity, collections).
- Build and manage a 12–24\-month revenue and pipeline plan across existing accounts and new target logos, in partnership with Sales and Finance.
2\. Client \& CXO Expansion
- For existing clients, act as growth partner to account teams and the COO, identifying 2–3 new Transformation/AI/digital value plays per year and driving them from pilot to scaled program.
- Systematically expand relationships from COO to broader CXO suite (CFO, CMO/Chief Experience, CIO/CDO, clinical leaders) for existing and target new logos around a cohesive value narrative.
3\. Industry Solutions \& Transformation Consulting (Proactive Deals)
- Define and evolve a focused set of EXL Healthcare industry solutions and transformation themes (e.g., care management modernization, digital front door, payment integrity, digital clinical operations).
- Lead targeted consulting‑style engagements to shape proactive, non‑RFP transformation proposals: frame client problems, quantify value, design multi‑year roadmaps, and originate deals that EXL takes to clients before formal RFPs.
- Work with analytics/AI, digital and operations leaders to ensure these solutions are implementable, scalable, and differentiated.
4\. Rapid Demos \& Prototyping
- Be the ‘Forward Deployed Engineering’ Specialist to the strategic customers by coaching and mentoring a small India based “demo studio” team that builds 2–3 days turnaround demos and light prototypes tailored to specific clients and two clear value propositions per pursuit (e.g., denial‑related cost reduction, digital engagement uplift).
- Integrate demos into pursuits, CXO workshops and RFP responses to differentiate EXL’s “data \+ AI \+ operations” story and shorten sales cycles.
5\. Internal Alignment \& Operating Rhythm
- Coordinate with Healthcare BU leadership, Analytics/AI, Delivery and Marketing so solutions, demos and narratives are consistent and field‑ready.
- Establish a simple cadence of portfolio, pipeline and solution reviews to track progress, risks and learnings, and to scale successful patterns across accounts.
- Qualifications: 20\+ years in healthcare, analytics, digital or consulting roles, including leadership of complex, multi‑stakeholder transformation programs for payers and/or providers.
- Deep understanding of US healthcare economics, operations and regulatory environment, with hands‑on experience in at least two areas such as care management, utilization management, Claims adjudication, payment integrity, revenue cycle or member engagement.
- Strong working knowledge of data, analytics and AI/ML concepts and their application in healthcare operations; able to translate technical capabilities into clear business value propositions.
- Proven track record in consultative selling and solutioning, originating and closing large, multi‑year transformation deals (including proactive, non‑RFP opportunities).
- Demonstrated ability to build and grow C‑suite relationships (COO, CFO, CMO/Chief Experience, CIO/CDO, clinical leaders) and to navigate complex, matrixed client organizations.
- Exceptional communication and storytelling skills, with the ability to synthesize complex ideas into concise, compelling narratives and decision‑ready materials for internal and client executives.
- High emotional intelligence and collaboration skills; comfortable leading cross‑functional teams, managing tension, and aligning diverse stakeholders around shared outcomes.
- Entrepreneurial, hands‑on mindset with willingness to prototype, iterate and learn quickly while maintaining strong commercial discipline.
Salary range for this position is $275,000 \- $300,000 USD.
*The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher\-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.*
Salary Context
This $275K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($287K) sits 34% above the category median. Disclosed range: $275K to $300K.
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.
EXL Service AI Hiring
EXL Service has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, FL, US, US. Compensation range: $125K - $300K.
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
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