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By joining Sedgwick, you'll be part of something truly meaningful. It’s what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there’s no limit to what you can achieve.
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VP Product Management - AI PRIMARY PURPOSE
Leads enterprise product strategy, portfolio direction, and delivery execution across multiple product domains. Responsible for aligning product investments to business objectives, driving transformational initiatives, and ensuring products deliver measurable value for clients, colleagues, and the organization. Partners closely with business, technology, and operational leaders to shape roadmap priorities, accelerate innovation, and drive adoption of AI-enabled solutions while ensuring scalable, high-quality delivery aligned with strategic goals.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
Product Strategy & Roadmap
- Establishes and owns enterprise-level product vision and multi-year roadmaps across product portfolios, aligning investments and priorities with corporate strategy, growth objectives, and platform direction.
- Leads strategic planning and investment prioritization, balancing innovation, client value, operational needs, technical health, and long-term scalability.
- Partners with business, technology, and operational leaders to align priorities, funding, sequencing, and product outcomes across the enterprise.
- Defines and monitors enterprise-level product objectives and performance measures, communicating strategy, progress, risks, and business impact to executive stakeholders.
Execution & Transformation Delivery
- Provides executive oversight of product delivery, portfolio execution, and capacity planning, ensuring alignment between product strategy, technical execution, client needs, and business outcomes.
- Leads execution of high-value transformational initiatives, translating business objectives into actionable plans, measurable outcomes, and sustainable operating practices.
- Collaborates across internal business leaders, operations teams, technology partners, and external stakeholders to drive alignment, remove barriers, and accelerate delivery of strategic priorities.
- Engages directly with clients and key stakeholders to gather insights, validate solutions, and ensure products and initiatives deliver meaningful business value.
- Owns accountability for product performance, business outcomes, operational efficiency, client impact, and portfolio-level key performance indicators.
- Ensures appropriate prioritization and execution of technical health, platform modernization, risk mitigation, and non-functional requirements alongside product enhancements and new development.
Enterprise Leadership & Innovation
- Maintains an enterprise-wide view of business processes, operating models, platforms, data, and system ecosystems impacting product strategy and execution.
- Evaluates and approves major product, platform, and operating model changes, assessing enterprise impact, risks, dependencies, adoption, and return on investment.
- Acts as a senior escalation point, resolving complex cross-functional challenges involving priorities, stakeholder alignment, organizational change, dependencies, and delivery risk.
- Identifies and champions opportunities to leverage AI, automation, and emerging technologies to improve customer experience, operational efficiency, decision-making, and business performance.
Partners across business and technology teams throughout the AI Product Development Lifecycle (PDLC), supporting the design, implementation, governance, adoption, and continuous improvement of AI-enabled solutions. *
ADDITIONAL FUNCTIONS AND RESPONSIBILITIES
- Performs other duties as assigned.
- Travels as required.
QUALIFICATIONS
Education & Licensing
- Bachelor's degree from an accredited college or university required.
- Advanced degree (MBA or equivalent) preferred.
- Relevant certifications in Product Management, Agile, AI, Digital Transformation, or related disciplines preferred.
Experience
- Ten (10+) years of progressive experience in product strategy, product management, portfolio leadership, or related disciplines.
- Demonstrated experience leading Directors, senior product leaders, and large, complex product organizations.
- Proven success managing enterprise-level product portfolios, strategic investments, and large-scale transformation initiatives.
- Experience driving cross-functional execution across business, operations, and technology organizations.
- Experience leading or supporting AI-enabled products, capabilities, or transformation initiatives preferred.
Skills & Knowledge
- Expertise in enterprise product strategy, portfolio management, and investment governance.
- Ability to translate corporate strategy into product vision, operating models, roadmaps, and execution frameworks.
- Strong executive judgment balancing growth, client value, technical health, risk, and financial outcomes.
- Advanced ability to lead complex, high-stakes discussions with executive business and technology leaders.
- Deep understanding of modern product operating models, technology platforms, architecture considerations, and system ecosystems.
- Knowledge of AI capabilities, AI governance principles, and AI Product Development Lifecycle (PDLC) practices.
- Proven capability to lead leaders, influence enterprise decisions, build high-performing teams, and drive organizational change.
Work Environment
When applicable and appropriate, consideration will be given to reasonable accommodations.
Mental: Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem solving, analysis, and discretion; ability to handle work-related stress, manage multiple priorities simultaneously, and meet deadlines.
Physical: Computer keyboarding; travel as required.
Auditory/Visual: Hearing, vision, and verbal communication.
The statements contained in this document are intended to describe the general nature and level of work being performed by a colleague assigned to this position. They are not intended to constitute a comprehensive list of functions, duties, or local variances. Management retains the discretion to add to or modify the duties of the position at any time.
Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace.
If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.
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 Sedgwick, 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 $212,000 based on 11,857 positions with disclosed compensation.
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.
Sedgwick AI Hiring
Sedgwick has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span FL, US, TX, US, TN, US.
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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