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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
AI Platform Administrator ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
Feature, connector, app, and agent governance
- Maintain the allowlist of approved connectors, apps, plugins, and extensions across all three platforms, and route every new request through security review and the standard IT governance process before enablement.
- Administer Claude connectors and the organizational Skills catalog, including version control, ownership assignment, and retirement of superseded Skills.
- Administer ChatGPT Enterprise workspace settings, including plugin and app availability, action and approval controls, custom GPT policy, and Workspace Agents settings — which are disabled by default and require explicit risk review before enablement, particularly where agents would publish using personal or shared connections.
- Administer Microsoft 365 Copilot controls in the Microsoft 365 admin center, including agent enablement, blocking, and assignment through integrated apps, connector availability, and coordination with the Power Platform team on Copilot Studio publishing and environment policy.
- Maintain a reviewed inventory of agents, custom GPTs, and Skills in each tenant on a scheduled cadence, so that capabilities introduced through vendor app updates or self-service authoring do not bypass review.
- Configure sharing and visibility defaults, including Claude project visibility and restriction of public projects, ChatGPT workspace sharing policy, and Copilot agent sharing scope.
- Administer grounding and search data sources — Claude Enterprise Search, ChatGPT connectors, and Copilot connectors — only where the data owner has provided documented approval and source-level permissions are verified.
- Execute phased feature rollout — core capabilities, then intelligence features, then integrations, then automation and agents — so that governance posture is reassessed at each stage rather than after the fact.
Coordinate with End User Computing on managed deployment of desktop, browser, and Office add-in clients, including MDM packaging and extension controls. *
Level 2 triage and support operations
- Serve as the designated Level 2 escalation point for all incidents and service requests relating to Claude, ChatGPT, and Copilot routed from the Service Desk (Level 1), owning queue triage, prioritization, and adherence to published service levels.
- Triage access and authentication failures and partner with IAM to resolve them, including federation and assertion errors, provisioning and group-membership gaps, entitlement and seat-type mismatches, and orphaned or duplicated accounts; own the platform-side diagnosis and escalate the provisioning or identity-side fix to the accountable team.
- Troubleshoot Claude Code client issues, including enterprise authentication, re-authentication after account or client changes, client version updates, migration from API key–based access to enterprise seats, and terminal and operating-system dependencies across Windows, macOS, and Linux/WSL.
- Troubleshoot ChatGPT Enterprise desktop, Office add-in, and developer tooling issues, including workspace and seat scoping, plugin and connector authorization, and role-based access to restricted capabilities.
- Troubleshoot Microsoft 365 Copilot issues, including license and eligibility checks, agent availability, grounding and indexing behavior across SharePoint and Exchange content, add-in availability in Office clients, and permission-inherited results.
- Resolve connector and integration authorization failures, file upload and format issues, error conditions, and content-filtering outcomes, distinguishing product behavior from configuration defects and from user expectation gaps.
- Reproduce, document, and escalate confirmed platform defects to Anthropic, OpenAI, or Microsoft support with complete diagnostics; track escalations to closure and communicate status to requesters and stakeholders.
- Author and maintain the support knowledge base, Level 1 triage scripts, known-error records, and per-platform runbooks so that routine volume is resolved at first contact rather than escalated.
- Maintain the Level 1 / Level 2 / vendor escalation model, coverage rotation, and escalation matrix for each platform; contribute to problem management, root cause analysis, and post-incident reviews.
Support major-incident communications for platform availability events, including user-facing status updates and coordination with the Service Desk. *
Governance, compliance, and audit
- Monitor audit and activity logging across platforms and support the flow of log data into enterprise security monitoring — Claude audit logs, the ChatGPT Enterprise compliance logs platform, and Microsoft Purview audit for Copilot and agent activity.
- Administer data retention settings in line with Sedgwick records management, Legal, and Privacy requirements, and document the approved retention position for each platform.
- Support authorized use of vendor compliance APIs and organization data export for data loss prevention, eDiscovery, and regulatory requests, acting only on validated requests from Legal, Privacy, or Information Security.
- Maintain the evidence set required for third-party risk, security, and privacy reviews, including certification artifacts, data processing agreements, and any business associate agreement obligations, and track vendor control changes that affect Sedgwick’s approved posture.
- Enforce acceptable use and data-handling standards, including restrictions on entering confidential, regulated, claimant, employee, or production data into any AI platform outside approved controls, and escalate suspected violations.
- Maintain a documented configuration baseline per platform and route changes through enterprise change control, with rollback considerations captured for privileged or high-impact changes.
Support internal and external audit requests with documented configuration, entitlement, and change evidence. *
Adoption analytics and reporting
- Produce recurring adoption reporting from each platform’s native analytics — Claude usage and Claude Code analytics, ChatGPT workspace analytics and usage exports, and Copilot usage reporting and analytics — including weekly active users as a percentage of provisioned seats, activity per user, and feature adoption.
- Consolidate platform metrics into a single cross-platform adoption view so that leadership can compare uptake and support burden on a consistent basis.
- Administer periodic user surveys capturing self-reported time saved, tasks augmented, and satisfaction, and pair quantitative results with qualitative examples from team leads.
- Deliver monthly operational reporting and quarterly business review inputs to AI Technology leadership.
Track support volume, ticket aging, first-contact resolution, and recurring failure themes by platform, and convert findings into configuration or enablement actions. *
Enablement and change management support
- Support all-staff introductory sessions, department-level enablement built around real workflows, and recurring office hours during initial rollout and for each new team onboarded.
- Maintain clear guidance on which platform to use for which task, so that overlapping capabilities do not create user confusion or duplicated licensing.
- Operate the internal support and community channel, maintain the self-service resource hub, and curate a library of approved prompts, Skills, GPTs, and agents with the AI Center of Excellence.
- Enable and support the champion network with early access, advanced guidance, and demonstration material.
- Integrate AI tooling onboarding into standard new hire orientation and, where applicable, package training into trackable LMS courses to monitor enablement coverage.
Deliver a brief retrospective at each rollout phase covering emerging use cases, remaining barriers, and changes required for the next phase. *
ADDITIONAL FUNCTIONS and RESPONSIBILITIES
- Performs other duties as assigned.
Travel as required. *
Remote role, open to any U.S. State.
QUALIFICATIONS
Education & Licensing
Bachelor’s degree in information technology, computer science, or a related field, or equivalent professional experience.
Experience
Five (5) or more years administering enterprise SaaS platforms in a regulated or large-enterprise environment, including at least two years as a named Level 2 escalation owner.
Skills & Knowledge
- Hands-on administration experience in a Microsoft 365 tenant, including the Microsoft 365 admin center, licensing and service plan assignment, and integrated app or add-in governance.
- Working knowledge of enterprise single sign-on and SCIM provisioning concepts sufficient to triage access failures accurately and partner effectively with IAM, without owning identity provider configuration.
- Demonstrated experience applying least-privilege principles to platform settings and integrations, and operating within formal change control.
- Working experience in an ITSM platform (ServiceNow preferred) across incident, request, problem, and knowledge management, operating to defined service levels.
- Ability to interpret audit logs and administrative analytics, use REST APIs for administrative tasks, and write or adapt basic scripts (PowerShell or Python).
- Command-line and terminal proficiency sufficient to support developer tooling installation and authentication across Windows, macOS, and Linux/WSL.
- Strong technical writing skills, with a track record of producing runbooks, knowledge articles, and operating policies that reduce escalation volume.
Demonstrated ability to work across Information Security, IAM, Legal, Privacy, and business stakeholders, and to communicate technical constraints to non-technical audiences. *
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
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. Mid-level AI roles across all categories have a median of $193,200.
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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