Interested in this AI/ML Engineer role at Sedgwick?
Apply Now →Skills & Technologies
About This Role
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.
Newsweek Recognizes Sedgwick as America’s Greatest Workplaces National Top Companies
Certified as a Great Place to Work®
Fortune Best Workplaces in Financial Services & Insurance
VP AI Engineering - Enterprise PRIMARY PURPOSE : The Vice President, Generative (Gen) AI Engineering Enterprise, provides executive leadership, strategic direction, and accountability for the enterprise data and AI engineering ecosystem that enables the organization’s most advanced analytics, data science, and generative AI initiatives. This role owns the vision, governance, and execution model for the enterprise data supply chain that powers machine learning platforms, feature stores, and production‑grade generative AI capabilities.
As a senior leader, the Vice President establishes and scales the engineering strategy required to transform legacy and modern data assets into trusted, AI‑ready enterprise capabilities. The role ensures that data and AI engineering investments are aligned to business outcomes, regulatory expectations, and long‑term architectural sustainability across on‑premise and multi‑cloud environments, including Snowflake and AWS/Azure AI ecosystems.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
- Defines and leads the enterprise strategy for data and generative AI engineering, establishing standards, roadmaps, and operating models that support analytics, machine learning, and AI at scale.
- Provides executive oversight for the design, delivery, and operation of enterprise ETL/ELT platforms that integrate legacy on‑premise systems (including mainframe, SQL Server, and DB2) with modern cloud data and AI platforms.
- Establishes governance and accountability for feature stores, analytically optimized datasets, and AI‑ready data products to ensure reproducibility, regulatory compliance, and production reliability.
- Sets direction for generative AI enablement, including enterprise approaches for vectorization, embeddings, retrieval‑augmented generation (RAG), and secure consumption of structured and unstructured data.
- Owns the enterprise Snowflake strategy, including architectural patterns, performance standards, cost governance, and integration with cloud‑based AI services across AWS and Azure.
- Leads cross‑functional collaboration with Technology, Security, Infrastructure, Legal, Risk, and Compliance leaders to ensure data and AI engineering practices meet security, privacy, and regulatory requirements.
- Establishes executive‑level operating metrics and financial discipline to ensure data and AI engineering investments deliver measurable business value and return on investment.
- Provides leadership and direction to engineering leaders and teams, setting priorities, removing organizational barriers, and ensuring consistent execution across portfolios and initiatives.
- Serves as a senior advisor to executive leadership on data, AI, and generative AI capabilities, risks, and opportunities, translating technical complexity into business‑level insights and decisions.
- Drives organizational readiness by shaping talent strategy, capability development, and succession planning for data and AI engineering roles across the enterprise.
- Job Responsibilities + Lead AI engineering for enterprise functions including HR, Marketing, Finance, Legal, and other corporate functions. + Identify and deliver high-value LLM, agentic AI, workflow automation, and knowledge-assistant use cases. + Build reusable AI capabilities, agents, APIs, connectors, and workflow integrations rather than isolated point solutions. + Own the enterprise administration and operational management of ChatGPT Enterprise and Claude . + Manage user access, licenses, roles, permissions, workspaces, and enterprise configuration for these platforms. + Establish and manage approved connectors and integrations to enterprise data and applications. + Partner with Security, Privacy, Legal, and AI Governance to define appropriate access controls and data-use guardrails. + Establish standards for enterprise use of GPTs, agents, projects, connectors, and other platform capabilities. + Monitor adoption, utilization, license consumption, token/API usage, and overall platform effectiveness. + Partner closely with Finance and Procurement to manage AI platform spend, forecasting, chargeback/showback, license optimization, and vendor commitments. + Develop cost-management practices to identify unused licenses, excessive consumption, duplicated capabilities, and opportunities for optimization. + Own relationships with OpenAI, Anthropic, and other relevant enterprise AI technology providers. + Evaluate new platform capabilities and determine when they should be enabled, restricted, piloted, or adopted broadly. + Ensure enterprise AI tools align with Sedgwick's broader AI architecture and do not create unmanaged technology or data silos. + Establish operational dashboards for adoption, usage, cost, risk, and business value. + Lead a small engineering/platform team responsible for enterprise AI solutions, integrations, administration, and support. + Partner with business leaders to drive responsible adoption and measurable productivity improvement from enterprise AI.
- Strong balance of technical depth, operational discipline, and business orientation
ADDITIONAL FUNCTIONS and RESPONSIBILITIES
- Performs other duties as assigned.
- Travel as required.
SUPERVISORY RESPONSIBILITIES
- Provides support, guidance, leadership and motivation to promote maximum performance.
- Administers company personnel policies in all areas and follows company staffing standards and training recommendations.
- Interviews, hires and establishes colleague performance development plans; conducts colleague performance discussions.
QUALIFICATIONS
Education & Licensing
Bachelor’s degree in Computer Science, Data Engineering, Artificial Intelligence or a related field from an accredited college or university required. Masters Degree preferred.
- Qualifications + 10+ years of engineering, platform, or enterprise technology experience with 5+ years in technical leadership. + Strong hands-on understanding of LLMs, agentic AI, RAG, APIs, workflow automation, and enterprise integration . + Experience building AI solutions for corporate functions such as HR, Marketing, Finance, or Legal. + Experience administering enterprise SaaS or AI platforms at significant organizational scale. + Strong understanding of identity, SSO, RBAC, connectors, APIs, data permissions, and enterprise security. + Experience with ChatGPT Enterprise, Claude Enterprise, Microsoft Copilot, or comparable enterprise AI platforms strongly preferred. + Strong AWS and/or Azure knowledge and understanding of how enterprise AI platforms integrate with cloud environments. + Experience establishing AI governance, access controls, monitoring, and responsible-use standards. + Strong financial management skills with experience managing technology consumption, licenses, vendor spend, and forecasts. Ability to work effectively across Engineering, Security, Finance, Procurement, Legal, HR, Marketing, and executive leadership. +
Experience
Ten (10) years of progressive experience in data engineering, analytics platforms, or AI‑related technologies, including senior leadership responsibility for enterprise‑scale data or AI initiatives, or an equivalent combination of education and experience required.
Skills & Knowledge
- Executive‑level expertise in modern data architectures, cloud data platforms, and AI enablement, including Snowflake and AWS/Azure ecosystems
- Proven experience establishing enterprise governance models for data, analytics, and AI systems
- Strong understanding of machine learning and generative AI data requirements, including feature engineering, vector databases, and RAG‑based architectures
- Ability to set technical direction without direct hands‑on execution, guiding senior engineers and leaders through complex architectural and delivery decisions
- Experience managing large, cross‑functional initiatives with significant financial and organizational impact
- Strong financial acumen, including budgeting, investment prioritization, and ROI measurement for technology initiatives
- Exceptional executive communication skills, with the ability to influence senior leaders and align stakeholders across business and technology domains
- Demonstrated ability to lead through ambiguity, scale new capabilities, and drive enterprise change
- Ability to meet or exceed Performance Competencies
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; ability to handle multiple priorities simultaneously; and ability to meet deadlines
Physical : Computer keyboarding, travel as required
Auditory/Visual : Hearing, vision and talking
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 description. They are not intended to constitute a comprehensive list of functions, duties, or local variances. Management retains the discretion to add or to change the duties of the position at any time.
Position is based in the U.S. and is remote.
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 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.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.