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About This Role
This is your opportunity to join AXIS Capital – a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly maintain an entrepreneurial, disciplined and ethical corporate culture. As a member of AXIS, you join a team that is among the best in the industry.
At AXIS, we believe that we are only as strong as our people. We strive to create an inclusive and welcoming culture where employees of all backgrounds and from all walks of life feel comfortable and empowered to be themselves. This means that we bring our whole selves to work.
All qualified applicants will receive consideration for employment without regard to any protected characteristic, including age, color, disability, ethnicity, gender identity, marital status, national origin, pregnancy, race, religion, sex, sexual orientation, veteran status, or any basis prohibited by the laws that govern its operations.
Design, develop, and operationalize secure agentic AI capabilities in risk & security operations
- Build, deploy, and maintain AI-powered security agents to augment detection, investigation, and response workflows
- Identify high-value use cases where AI agents can accelerate analyst productivity and improve outcomes
- Continuously validate agent performance, accuracy, and reliability in real-world operations
Ensure responsible and secure use of AI across risk & security operations
- Enforce strong security controls for enterprise AI platforms (e.g., Microsoft Copilot, Copilot Studio, Azure AI Foundry)
- Mitigate AI-specific risks including data leakage, prompt injection, oversharing, and unauthorized access
- Establish evaluation practices for agent accuracy, hallucination risk, false positives/false negatives, failure modes, explainability, audit logging, and measurable operational impact.
- Ensure human-in-the-loop oversight to include analysts challenge, refine, and retrain AI-driven outcomes
Implement and manage data protection controls for AI and enterprise data
- Deploy and optimize Microsoft Purview capabilities (DLP, Information Protection, Insider Risk) across M365 and AI platforms
- Implement DLP controls for AI interactions and outputs to prevent sensitive data exposure
- Monitor and tune data protection policies to meet regulatory and business requirements
Secure AI agents, automation, and data flows
- Define and enforce access controls, data handling policies, and auditability for AI agents and automated workflows
- Ensure all AI-driven processes align with Zero Trust and least privilege principles
- Evaluate and onboard new AI tools and agents with a security-first approach
Operational monitoring, detection, and response
- Monitor AI platform activity and data usage to detect anomalies, misuse, or policy violations
- Support incident response and investigations involving AI systems and data exposure scenarios
- Integrate AI agent workflows directly with SIEM/XDR platforms to allow autonomous context enrichment and preliminary incident triage.
- Support enterprise rollout of AI governance and data security programs
About You:
We encourage you to bring your own experience and expertise to the table, so while there are some qualifications and experiences, we need you to have, we are open to discussing how your individual knowledge might lend itself to fulfilling this role and help us achieve our goals.
What you need to have:
- Bachelor’s degree in Information Technology, Cybersecurity, or related field (Master’s preferred), OR equivalent experience leading enterprise-scale security engineering initiatives
- 5+ years of progressive experience in cybersecurity, data protection, or security engineering
- Proven experience designing and securing enterprise AI platforms, including: + Microsoft Copilot, Copilot Studio, and Azure AI Foundry + Governance, data grounding controls, prompt injection protections, and AI risk management + Development or oversight of security controls for AI-driven workflows and autonomous/agent-based systems.
- Hands-on expertise with Microsoft security and compliance ecosystems, including: + Microsoft Purview (end-to-end deployment, governance, and optimization) + Microsoft 365, Azure, and Entra ID security architecture + Collaboration platforms (Exchange, SharePoint, OneDrive, Teams) with a strong focus on data protection and insider risk
- Proven expertise in Data Security and Protection, including: + Data Loss Prevention (DLP) strategy, engineering, and tuning at scale + Data classification, sensitivity labeling, and lifecycle management + Insider Risk Management and Information Protection programs
- Demonstrated ability to architect and lead enterprise security solutions, including: + Defining strategy, roadmaps, and reference architectures + Driving cross-functional initiatives across Security, IT, Legal, and Business teams + Acting as a technical authority and escalation point for complex security challenges
- Strong development and automation capabilities, including: + Proficiency in Python, PowerShell, TypeScript or similar languages; secure API integration; Microsoft Graph and security platform APIs; CI/CD practices; test automation; and experience deploying AI-enabled workflows using Azure Functions, Logic Apps, or comparable cloud-native services. + Building automation pipelines for detection, response, and data governance workflows
- Experience with security analytics and telemetry, including: + Interpreting logs and signals from Microsoft 365, Azure, endpoints, and AI systems + Supporting detection engineering and threat-informed defense strategies
- Ability to lead in complex, ambiguous environments, including: + Rapidly assessing risk and prioritizing high-impact outcomes + Driving technical decision-making with incomplete information + Mentoring junior engineers and elevating overall team capability
What we prefer you to have:
- Experience with prompt engineering techniques or semantic caching.
- A firm understanding of the OWASP Top 10 for LLMs.
- Experience working with Data Security Posture Management (DSPM) tools.
- Experience supporting data leakage or insider threat programs
- Relevant Cybersecurity certifications + AI-102 (Azure AI Engineer Associate) + AI-900 (Microsoft Azure AI Fundamentals) + SC-400 (Information Protection Administrator) + MS-102 (Microsoft 365 Administrator) + CompTIA Security+ + CompTIA SecAI+
Role Factors:
In this role, you will support enterprise-wide data protection and governance initiatives leveraging Microsoft 365 platforms. This includes safeguarding sensitive data, enabling secure collaboration, and supporting emerging AI technologies like Copilot by ensuring proper data classification and protection standards are in place. The work is highly visible and directly impacts how the organization securely manages and scales its data environment.
What we offer:
For this position, we currently expect to offer a base salary in the range of $90K - $110K Your salary offer will be based on an assessment of a variety of factors including your specific experience and work location.
In addition, you will be offered competitive target incentive compensation, with awards based on overall corporate and individual performance. On top of this, you will be eligible for a comprehensive and competitive benefits package which includes medical plans for you and your family, health and wellness programs, retirement plans, tuition reimbursement, paid vacation, and much more.
Where this role is based in the United States of America, this role is exempt for FLSA purposes.
This posting is for an existing vacancy.
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AXIS Capital, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AXIS Capital AI Hiring
AXIS Capital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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