Interested in this AI/ML Engineer role at Berkley?
Apply Now →Skills & Technologies
About This Role
Company Details:
Company URL: https://www.berkleytechnologyservices.com/
Berkley Technology Services (BTS) is the dynamic technology solution for W. R. Berkley Corporation, a Fortune 500 Commercial Lines Insurance Company. With key locations in Urbandale, IA and Wilmington, DE, BTS provides innovative and customer\-focused IT solutions to the majority of WRBC’s 60\+ operating units across the globe. BTS’s wide reach ensures that ideas and opinions are considered at every level of the organization to guarantee we find the best solutions possible.
Driven by a commitment to collaboration, BTS acts as consultants to our customers and Operating Units by providing comprehensive solutions that not only address the challenge at hand, but proactively plan for the “*What’s Next*” in our industry and beyond.
With a culture centered on innovation and entrepreneurial spirit, BTS stands as a community of technology leaders with eyes toward the future \- leaders who truly care about growing not only their team members, but themselves, and take pride in their employees who shine. BTS offers endless ways to get involved and have the chance to grow your career into a wide range of roles you'd never known existed. Come join us as we push forward into the future of industry leading technological solutions. *Berkley Technology Services: Right Team, Right Technology, Simple and Secure.*
Responsibilities:
This role is focused on building, testing, and operating AI\-enabled features and services. Senior AI Engineers deliver production code: implementing services and agentic workflows, wiring up retrieval\-augmented generation (RAG) pipelines, integrating with web applications, and instrumenting systems for reliability, security, and cost.
- Build Python services and microservices (APIs, workers) that expose AI capabilities; write clean, tested, maintainable code.
- Implement end\-to\-end RAG pipelines: connectors, parsing, chunking, embeddings, indexing, and retrieval using Azure AI Search and/or Pinecone.
- Create and operate agentic workflows with LangGraph, n8n, or Agent Development Kit; iterate on prompts/flows and automate offline/online evaluations
- Integrate AI into web applications (REST/GraphQL, events) with attention to input/output validation, rate limiting, and graceful degradation.
- Own CI/CD and containerization for your services; add telemetry (logs/metrics/traces), dashboards, and alerts; participate in on\-call/incident response
- Apply Responsible AI, data protection, and access controls; contribute guardrails (filters, red\-teaming, PII handling) in code.
- Collaborate with analysts, QA, and product owners to refine requirements; demo increments and incorporate feedback in an agile cadence
- Travel for this position is approximately 5\-10%.
Qualifications:
- 5\+ years of professional software engineering experience.
- 3\+ years of hands\-on applied AI/LLM engineering delivering agentic AI production systems.
- Proficiency in Python and modern engineering practices (testing, linting, typing, packaging, CI).
- Experience with Cloud AI platforms like Azure AI Foundry, GCP Vertex and AWS Bedrock.
- Hands\-on experience with vector databases and search algorithms.
- Solid experience with containers and CI/CD; practical AI observability (logs/metrics/traces) and production support mindset.
- Developed multi\-agent systems using MCP and A2A technologies.
- Hands on experience with Agentic AI development tools like Cursor, Claudecode and Github copilot.
- Clear, concise communicator able to collaborate with analysts, QA, architects, and business stakeholders.
- Experience with AI Observability in platforms like Datadog and Langsmith.
- Bachelor’s degree with emphasis in related field or equivalent experience.
Qualifications that are not required but are a plus
- Familiarity with knowledge graphs (e.g., Neo4j) and graph queries (e.g., Cypher)
- Experience leveraging and training NLP models
- Experience fine\-tuning LLMs and VLMs
- Experience with LLM evaluation frameworks like DeepEval and RAGAs
Behavioral Core Competencies* Critical Thinking
- Customer Service Oriented
- Technically Astute
- Business Knowledge
- Influential
- Conceptual Thinking
- Personal Ownership
The Company is an equal employment opportunity employer.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Berkley, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Berkley AI Hiring
Berkley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US.
Location Context
AI roles in Los Angeles pay a median of $214,112 across 708 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.