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About This Role
Job Description: About EXLdecision.ai
EXLdecision.ai is EXL’s agentic data science platform that helps enterprises build, validate, and monitor AI-driven decisions faster and better with high degree of transparency. It combines LLMs, agents, and EXL’s domain IP to compress the analytics-driven decisioning lifecycle from months to weeks across use cases such as credit risk, fraud, and marketing analytics.
Role Overview
As VP2 of Product & Growth Strategy, you are the commercial and strategic leader for EXLdecision.ai — a “Product-CEO” role accountable for the product’s vision, P&L, and market success. You will translate an AI-native vision into product experiences that measurably accelerates our customers’ analytics decision lifecycle including model-development, strategy development, etc.
You will run EXLdecision.ai as a growth-oriented “business within a business,” balancing entrepreneurial speed with enterprise discipline, and owning the outcomes that define enterprise value: revenue, margin, adoption, and differentiation.
What You'll Own in the First 12 Months
- Convert 6–8 clients into successful POCs, with a defined path from proof to paid deployment
- Launch GTM motion — sharpened target segments, pricing, and packaging for the agentic suite
- Build the product-led growth engine, with instrumented activation, adoption, and expansion metrics
- Define the multi-year product roadmap, grounded in customer-validated agentic use cases
Responsibilities: Key Responsibilities 1. Product Strategy, Vision & P&L
- Own the product trajectory: Evolve the long-term vision for EXLdecision.ai, embedding agentic AI into the core platform to solve critical challenges in analytical decisioning.
- Lead the P&L: Directly manage the product P&L — revenue growth, contribution margin, and financial health.
- Roadmap & execution: Translate company strategy into a multi-year roadmap aligned to Annual recurring revenue (ARR) expansion and enterprise value.
- Decide with data: Use customer signals and performance data to align product investment with ROI.
2. GTM, Growth & Market Expansion
- Commercial execution: Own the full go-to-market strategy — target segments, pricing models, and packaging for the agentic suite.
- Expansion strategy: Identify and prioritize high-leverage growth across decisioning domains (e.g., risk, fraud, credit, marketing analytics) and verticals (e.g., banking, insurance, healthcare, retail).
- Product-led growth: Design and scale a growth engine that improves adoption, expansion, and customer lifetime value.
- Cross-functional alignment: Partner with Marketing and Sales to sharpen positioning and ensure the roadmap drives acquisition and retention.
3. Sales Partnership & Executive Sponsorship
- Product evangelism: Serve as the senior external face of the product, supporting key deals and renewals by articulating unique value and differentiation.
- Customer leadership: Act as executive sponsor for strategic accounts, building relationships and ensuring value realization.
- Executive presence: Present product strategy and growth impact to C-suite and board stakeholders with confidence.
- Voice of the customer: Bring firsthand enterprise insights back into the product organization to ground decisions in real needs.
Qualifications: Required Qualifications
- 10+ years of progressive leadership in B2B SaaS , data products, or enterprise software, including a track record of scaling products in growth-stage environments.
- Hands-on experience building or shipping products that incorporate LLMs, RAG pipelines, and AI agents, with a demonstrated ability to rethink user experiences around AI capabilities.
- Demonstrated success in P&L ownership, pricing strategy, and product-led growth.
- Fluency in decision sciences, data analytics, or data modelling — enough to earn the trust of technical builders and enterprise buyers.
- Exceptional executive presence, with the ability to influence senior buyers such as Chief Data / Analytics / Risk / Marketing Officers, partners, and technical stakeholders.
Preferred Qualifications
- Experience taking an AI or data product from 0 to 1 or through a step-change in scale.
- Domain exposure in at least one of the EXL's core verticals - banking, insurance, healthcare and retail.
- MBA, Engineering or advanced degree.
- Base Compensation Range: $220,000- $280,000
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 $220K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $220K to $280K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EXL Service AI Hiring
EXL Service has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Jersey City, NJ, US, US. Compensation range: $120K - $280K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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