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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
Beazley Security has Claude and Microsoft Copilot deployed across the company. The next step is putting agents to work across every team in the business. This is our first hire dedicated to making that happen. You’ll work with operations, sales, finance, and other non-technical teams to find manual work worth automating. You’ll build agents in Claude and Copilot Studio, get them into daily use, and measure the time they save. Early on you’ll build most of them yourself, while teaching teams to build their own.
You’ll have a big say in what we tackle first and how we build it. This is the first hire for what we plan to grow into a team, so a lot of what you set up early will stick.
Beazley Security protects client data and systems for a living, so you’ll build security and privacy into every agent from the start. You’ll help us establish safeguards to ensure that agents can go into production quickly and safely, by building upon our existing agentic security tooling and capabilities.Responsibilities
- Spend time with teams across the business, watch how the work gets done, and pick the problems where an agent saves hours or removes a manual, error-prone step. Agree on what success looks like before you build.
- Turn a stream of AI ideas into a short, ranked backlog with a clear call on each: build it, help the team build it, or leave it alone.
- Estimate what each agent will cost to run, including licensing and token consumption. Use our pre-agreed guardrails so low-risk agents go live quickly, and bring in Security, IT, Legal, or Privacy when a use case needs a closer look.
- Design, build, test, and deploy AI agents and supporting automation using the Claude Platform and Microsoft Copilot Studio.
- Build a group of champions across the business, run hands-on build sessions and office hours, and create the templates and playbooks teams use to build their own agents safely.
- Help maintain a record of every agent in production with its owner, data sources, and access. Evaluate new AI tools when teams ask about them and recommend whether we adopt them.
Your First 90 Days
- Days 1 to 30: Get to know Beazley Security. Sit with at least three teams, map their most painful workflows, and build your first agent yourself. Get it into real use, even if it’s small.
- Days 31 to 60: Two or three agents in production, each measured against a baseline of “value”. A published intake process, and your first AI champions identified.
- Days 61 to 90: At least one team can point to manual work that’s gone. The agent register and build playbook are live.
- After that: Champions build their own agents using your patterns, and your results justify the next hire onto this team.
What You'll Have
- Executive buy-in and support. You report to our Chief Product and Technology Officer, who uses AI every day and has built several agents for internal use. Expect quick decisions and direct help when a project gets stuck.
- A first project. A business sponsor and workflow lined up before day one.
- A fast review path. Guardrails agreed with Security and Legal before you start, so low-risk agents don’t sit in a queue.
Qualifications
- You’ve built AI agents or automations that are in daily use, with low-code tools like Copilot Studio or with code (Python, TypeScript, and the Claude API).
- You have hands-on experience with prompt and context design, retrieval, tool use, and evaluating agent output, and you know where LLMs fail.
- You’ve integrated with REST APIs, MCP servers, connectors, and enterprise authentication.
- You’ve typically spent four or more years in a business-facing technical role such as solutions engineering, solutions consulting, business analysis, automation, or platform delivery.
- You can learn a finance or sales operations workflow from the people who run it and build something that improves it.
- You know when a use case needs a security or privacy review before it goes anywhere, and you can estimate what an agent will cost to run on Copilot credits or Claude API usage.
- You’ve run workshops or training for non-technical audiences, and you write clearly.
- You’d rather ship a small agent that works than constantly plan and iterate on a big one.
Preferred
- Experience in cybersecurity.
- Experience running an AI champions program, community of practice, or citizen developer program.
- Experience setting up lightweight AI guardrails, like an agent inventory or risk tiers, that keep teams moving quickly.
- Familiarity EU data protection requirements.
- When you apply, please include a link or a short screen recording of something you built.
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. Senior-level AI roles across all categories have a median of $229,300.
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.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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