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About This Role
Job Description: We are seeking an experienced Agentic AI Delivery Lead to drive enterprise-scale Agentic AI, Generative AI, Data Management, and Data Modernization programs. The role combines delivery leadership, solution architecture, cloud transformation, and stakeholder management to build secure, scalable, production-grade AI and data platforms on AWS. Property & Casualty (P&C) Insurance experience is mandatory.
Base Compensation Range: 170,000 - 200,000
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.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
Responsibilities: Key Responsibilities Agentic AI Strategy and Delivery
- Lead end-to-end delivery of Agentic AI and Generative AI initiatives from discovery and solutioning through deployment, adoption, and production support.
- Design and oversee autonomous and multi-agent solutions using large language models, retrieval-augmented generation, reasoning workflows, tool integration, and orchestration frameworks.
- Define AI transformation roadmaps, delivery plans, success metrics, governance standards, risk controls, and reusable accelerators.
- Guide the implementation of responsible AI practices covering security, privacy, explainability, human oversight, hallucination controls, and regulatory compliance.
Data Management and Data Modernization
- Lead legacy-to-cloud data modernization, data migration, platform consolidation, and enterprise data transformation programs.
- Define and implement data management capabilities across data governance, data quality, metadata management, master data management, data lineage, and lifecycle management.
- Oversee modernization of data lakes, data warehouses, lakehouse platforms, analytics ecosystems, and real-time data processing solutions.
- Ensure modern data foundations are scalable, trusted, secure, and ready to support AI, analytics, and reporting use cases.
- Drive DataOps practices, reusable ingestion and transformation frameworks, and standardized ETL/ELT delivery patterns.
AWS Cloud and Engineering Leadership
- Provide technical oversight for cloud-native data and AI platforms using AWS services such as S3, Glue, Redshift, EMR, Lambda, Step Functions, Lake Formation, SageMaker, DynamoDB, ECS/EKS, IAM, and CloudWatch.
- Drive architecture decisions across data engineering, APIs, microservices, event-driven integration, containerization, infrastructure as code, and cloud security.
- Establish CI/CD, DevOps, MLOps, observability, reliability, performance, and cost-optimization practices for production workloads.
- Partner with architects and engineering teams to develop scalable reference architectures and engineering standards.
Program and Stakeholder Leadership
- Manage cross-functional global teams including AI Engineers, Data Engineers, Data Scientists, Architects, Product Owners, Business Analysts, QA, DevOps, and domain SMEs.
- Own program planning, estimation, budgeting, staffing, delivery governance, dependency management, risk mitigation, and executive reporting.
- Facilitate business and technology workshops to identify high-value AI and data modernization opportunities.
- Communicate roadmaps, architecture decisions, delivery status, risks, and business outcomes to senior stakeholders and clients.
- Mentor teams and promote engineering excellence, delivery discipline, innovation, and reusable solution patterns.
P&C Insurance Transformation
- Partner with underwriting, claims, policy administration, actuarial, billing, risk, and customer-service teams to identify AI-led transformation opportunities.
- Lead data modernization and AI initiatives supporting underwriting automation, claims intelligence, fraud detection, pricing analytics, risk insights, and customer servicing.
- Align delivery with insurance data standards, governance expectations, privacy requirements, and regulatory controls.
Qualifications: Required Qualifications
- 12+ years of experience across Data and Analytics, AI/ML, Cloud Transformation, Enterprise Architecture, or Digital Transformation.
- 5+ years of experience leading large-scale data modernization, data management, cloud, AI, or analytics programs.
- Proven experience delivering enterprise AI, Generative AI, or Agentic AI solutions into production.
- Strong delivery leadership experience across globally distributed, cross-functional teams.
- Hands-on understanding of modern data architectures, data engineering, cloud-native design, APIs, security, DevOps, DataOps, and MLOps.
- Strong executive communication, client management, solutioning, estimation, governance, and risk-management skills.
Preferred Qualifications
- Property & Casualty (P&C) Insurance experience, including exposure to policy, claims, underwriting, actuarial, billing, risk, or insurance data ecosystems.
- Experience leading data migration, data management, or platform modernization programs within insurance organizations.
- Familiarity with Guidewire, Duck Creek, or other insurance core platforms.
- Experience with AI governance, model risk management, responsible AI, and regulated enterprise environments.
- PMP, SAFe, Scrum, AWS Solutions Architect, AWS Data Engineer, AWS Machine Learning, or relevant AI/data certifications.
- Business value realization and outcome measurement
Salary Context
This $170K-$200K range is above the median 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $170K to $200K.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 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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