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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
Principal AI Engineer Remote role open to all U.S. states.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
- Owns the technical outcome of a value-stream pod from problem shaping through production operation, including whether work contributes to shared capability or is incomplete.
- Partners directly with business transformation leadership, examiners, nurse reviewers, and operations — sitting with users, shaping ambiguous problems, and refusing work that would wire new capability onto a process known to be broken.
- Architects and personally builds production Generative AI agents, workflows, and applications, contributing code in Python and React rather than reviewing from a distance.
- Sets and enforces engineering standards for the pod: API-first and contract-first delivery, AWS and Terraform as the path to production, GitHub Actions as the CI/CD bar, evaluation and observability as release gates, and human confirmation on consequential claims actions.
- Accountable for whether the pod lands on shared substrate (context, documents, agents, assurance) or forks it; exercises stewardship and design-council authority to block duplicate builds.
- Defines interface terms with vendor delivery teams when the stream is co-developed: shared repositories in Sedgwick accounts, Sedgwick-owned contracts, shared definition of done.
- Establishes evaluation, provenance, and autonomy-ladder evidence for AI workflows — what may auto-complete, what must ask then do, and what is not ready — with the business owning thresholds.
- Leads technical solution design and architectural review for the stream; escalates conflicts with enterprise standards (identity, model egress, PHI, portability) rather than absorbing them quietly.
- Represents the pod on the AI Engineering design council; operates independently and is expected to converge with peer Principals without requiring SVP arbitration for ordinary technical disagreement.
- Provides technical guidance and mentoring to Senior and Junior AI Engineers; raises the pod’s bar on code quality, operational excellence, and forward-deployed practice.
- Leads complex troubleshooting, root cause analysis, and performance optimization for business-critical AI services and platforms.
Evaluates and recommends whether a capability should be built in-house, co-developed with a vendor, or not built — with a named owner and a removal path when a compromise is accepted. *
ADDITIONAL FUNCTIONS AND RESPONSIBILITIES ·
- Performs other duties as assigned.
Travel as required, including regular on-site work with claims or Managed Care operations and with vendor delivery teams. *
QUALIFICATIONS
Education & Licensing
Bachelor’s degree in Computer Science, Engineering, or a related quantitative discipline from an accredited college or university required. Master’s degree preferred.
Experience
Ten (10)+ or more years of software engineering experience, including at least four (4) years owning production Generative AI or applied-AI systems in the field — agents, workflows, or applications, including evaluation, failure handling, and a human review loop — or an equivalent combination of education and experience.Hands-on production experience on AWS, and hands-on Python, React, GitHub Actions, and Terraform, at a level sufficient to set the bar for a pod, not only meet it. Cloud certification or architecture-only experience without recent production code is not sufficient.Experience leading technical delivery for a value stream or equivalent without people-management authority.Experience in insurance, claims, healthcare, or another heavily regulated operational domain preferred.
Skills & Knowledge
- Expert full-stack engineering skills in Python and React, with strong SQL, APIs, and service design; still writes production code
- Deep, hands-on AWS, Terraform, and GitHub Actions — including identity, networking, environments, and operational recovery — sufficient to set standards and review others’ work
- Production command of Generative AI agents, workflows, and applications, including tool use, orchestration, evaluation, provenance, and human-in-the-loop patterns
- Ability to walk into a design session with examiners or operations leaders and leave with a shaped problem, scoped build, and explicit non-goals
- Ability to keep a pod on shared substrate and to stop a duplicate build without escalating every conflict
- Ability to set interface terms with vendors and internal platform teams while remaining a peer IC, not a manager of either
- Strong mentoring, collaboration, and technical communication skills with the ability to influence transformation leadership, architecture, security, and engineering peers
- Experience with machine learning, model fine-tuning, and/or graph databases (e.g. Neo4j) preferred
- Experience with PydanticAI (or equivalent agent framework), agent observability, and AI Ops / LLMOps a plus
Ability to meet or exceed Performance Competencies *
WORK ENVIRONMENT
When applicable and appropriate, consideration will be given to reasonable accommodation.
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
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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Sedgwick AI Hiring
Sedgwick has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span FL, US, TN, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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