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About This Role
About Beazley Security
Beazley Security is a global cybersecurity firm committed to helping clients enable advanced cyber defenses that reduce risk with quantifiable results. We’re comprised of top talent from private industry, government, intelligence, and law enforcement who are specialists in threat detection, incident response, digital forensics, offensive security, risk management, and cyber resilience. As a subsidiary of a specialty insurance giant, Beazley Insurance, we’ve been at the forefront of cyber insurance management and breach response activities for business clients in the US, UK, and Europe since 2017. As Beazley Security, the company will have an expanded scope, leveraging nearly two decades of cyber incident experience, a strong services division, and a business strategy focused on growth, to realize our goals and deliver benefits to clients.
As a company, we are committed to upholding our core values of Belonging, Integrity, Service, Accountability, and Curiosity. We believe these values are essential to creating a strong and inclusive workplace culture, as well as to deliver world-class cybersecurity solutions to our clients worldwide. As Beazley Security, these values will continue to thrive, with an extra emphasis on expansion of our capabilities and capacity in helping solve unique client challenges.
Summary
As the first Agentic Security Engineer on Beazley Security’s MDR Operations Team, you’ll help us build a new managed service: providing our clients with visibility, control, and response for the agents that run and execute on their endpoints and servers. You’ll work hands on with our agentic runtime control platform, helping us deploy it, continuously tune it, and ensure our analyst team is receiving actionable alerts that require immediate action and response.
Reporting directly to the Director of MDR Operations, this role blends platform engineering, policy design, detection engineering, and analyst enablement.
You’ll work with MDR analysts, Product, Software Engineering, Professional Services, and vendor partners to shape how the service is configured, how its findings are routed and reported, and how it expands its coverage as AI adoption and capability keeps moving.
Ideal candidates bring experience from MDR/MSSPs, Software Engineering, or security product companies, along with hands on experience with Agentic applications such as Claude Code, Codex, OpenCode, OpenClaw, or Hermes. You know how hooks, skills, plugins, and MCP servers work because you’ve used them, and you’ve considered the risks that they introduce.
Responsibilities
Platform Deployment & Management
- Deploy, configure, and maintain the AI security platforms that power our Managed AI Detection and Response solution, including rolling out the tooling through MDM or EDR and configuring the initial enforcement policies.
- Manage the endpoint enforcement actions we run through the platform agent, including software removal and removal of MCP server, shell, and database integrations, with per-client pre-authorization and exclusion lists in place so enforcement never takes out business-critical tooling.
- Monitor platform health, coverage, webhook delivery, and enforcement-action lifecycle state so gaps in visibility or delivery are caught before they become gaps in detection or missed reports.
AI Policy & Configuration Engineering
- Translate client AI acceptable use policies, data handling requirements, and risk appetite into enforceable platform configurations under an “allow, ask, and deny” enforcement model.
- Build and maintain standard enforcement baselines by client segment, including exclusion lists and pre-authorized response actions we can take on clients’ behalf.
- Tune client deployments and environment suppressions as usage patterns and feature adoptions evolve.
- Keep policy changes documented, reviewed, and easy to understand.
Detection, Response, and Automation.
- Help build and tune the detection and routing logic that sends real threats to analysts for investigation and everything else to scheduled client reporting.
- Use Python, platform APIs, and agentic tools like Claude Code to automate client onboarding, baseline policy deployment, enrichment, reporting, and common response actions so the service scales without adding labor.
Analyst Mentoring & Enablement
- Serve as the MDR team’s subject matter expert on AI risk, AI tooling, and the platform stack behind AIDR.
- Run and maintain analyst runbooks, promotion-trigger guidance, and training materials for analyst queue handling.
- Review analyst escalations with your peers to help identify areas of improvement or where additional guidance and training may be needed.
Technology Evaluation & Service Development
- Track the AI threat landscape using the frameworks the service maps to, including MITRE ATT&CK, MITRE ATLAS, the OWASP Agentic Top 10, and the OWASP Top 10 for LLM Applications, and turn what you learn into service capabilities.
- Partner with MDR leadership and Product Engineering teams on the AIDR roadmap, and helping design the service as it grows.
Required Qualifications
- 3+ years of experience in cybersecurity, including at least 1 year in an engineering detection engineering, or automation role, preferably within an MDR, MSSP, or security product company.
- Hands-on experience with generative AI tools, AI agents, and extensions such as hooks, skills, plugins, and MCP servers, along with a deep understanding of associated risks, including misalignment, unexpected behavior, prompt injection, credential exposure, and malicious skills or plugins.
- Hands-on experience configuring policy-driven security platforms such as SIEM, EDR, SOAR, DLP, CASB/SSE, or browser and endpoint agent security tools.
- Proficiency with scripting or programming languages such as Python or PowerShell and experience working with REST APIs and webhook-based integrations.
- Strong written and verbal communication skills, with the ability to explain technical risk clearly to analysts and clients.
- Experience mentoring, training, or reviewing the work of analysts or junior engineers.
Preferred Qualifications
- Experience with AI security, agent runtime control
- Familiarity with the OWASP Top 10 for LLM Applications, the OWASP Agentic Top 10, MITRE ATT&CK, MITRE ATLAS, NIST AI RMF, or ISO/IEC 42001.
- Experience administering or securing enterprise AI assistants, LLM platforms, or agent frameworks.
- Experience with enterprise EDR, SIEM, and cloud productivity security platforms.
- Relevant security, cloud security, or AI governance certifications such as Security+, CCSP, or IAPP AIGP.
Beazley Security Offers
- Competitive salary and bonus.
- Flexible working arrangements.
- Generous leave policies including 3 months paid parental.
- 100% of employee-only insurance premiums covered (healthcare, dental and vision).
- Up to 5% matched 401k contribution.
- Opportunities for career advancement and ongoing training.
- Participation in industry conferences and events.
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 Beazley Security, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Beazley Security AI Hiring
Beazley Security has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, West Hartford, CT, US.
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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