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About This Role
Who Are We?
Taking care of our customers, our communities and each other. That’s the Travelers Promise. By honoring this commitment, we have maintained our reputation as one of the best property casualty insurers in the industry for over 170 years. Join us to discover a culture that is rooted in innovation and thrives on collaboration. Imagine loving what you do and where you do it.
Compensation Overview
The annual base salary range provided for this position is a nationwide market range and represents a broad range of salaries for this role across the country. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. As part of our comprehensive compensation and benefits program, employees are also eligible for performance-based cash incentive awards.
Salary Range
$153,700.00 - $253,700.00
Target Openings
1
What Is the Opportunity?
The MLOps Lead will lead the advancement of Business Insurance's MLOps and LLMOps practices across the full modeling lifecycle, driving end-to-end automation, observability, and scalability of our predictive models. This role will lead a small team of engineers and own and execute a technical roadmap focused on deployment automation, feature management, model monitoring & observability, and tools for GenAI and agentic solutions. They will partner closely with data scientists, architects, and technology teams to help shape Business Insurance's MLOps strategy, streamline processes, and standardize implementation patterns to accelerate the delivery of high-quality, reliable ML and AI solutions at scale.
What Will You Do?
- Execute data strategies to support various consumption patterns and identify enterprise architecture, platform, and application infrastructure needs.
- Drive the operationalizing and automating of all capabilities to ensure secure, supported and scalable solutions.
- Present analysis and recommendations to help influence management and executive leadership decisions.
- Guide and coach senior team members to accelerate career development.
- Establish budgets, policies and practices with significant impact on area operations.
- Perform other duties as assigned.
What Will Our Ideal Candidate Have?
- Bachelor’s Degree in STEM related field or equivalent.
- Eight or more years of related work experience.
- Four or more years of team leadership experience.
- MLOps Leadership:Proven experience advancing MLOps implementation and deployment practices from foundational to mature operational states.
- Strategic Execution:Demonstrated ability to lead cross-functional execution while contributing to strategic planning and organizational direction.
- People Management:Experience managing technical teams of 3-4 direct reports, with capability to oversee Director-level personnel as the organization scales.
- AI/ML Expertise:Strong background in artificial intelligence and machine learning with depth exceeding traditional data engineering experience.
- Technical Proficiency:Hands-on experience with industry-standard ML frameworks and platforms, including:
+ Cloud ML Services (AWS SageMaker)
+ Data Science Platforms (Databricks)
+ Machine Learning Algorithms (Gradient Boosting Models)
+ Deep Learning Frameworks (PyTorch, TensorFlow)
+ DevOps Practices (CI/CD, Automated Deployment, Automated Testing)
- Future-Ready Skills:Interest and aptitude for emerging technologies including Large Language Models (LLM's), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) Systems.
- Subject matter expertise in data tools, techniques, and manipulation including cloud platforms, programming languages, and technology platforms.
What is a Must Have?
- Bachelor’s degree in computer science, related STEM field, or its equivalent in education and/or work experience.
- 7 additional years of data engineering experience.
- 2 years of technical leadership experience.
What Is in It for You?
- Health Insurance: Employees and their eligible family members – including spouses, domestic partners, and children – are eligible for coverage from the first day of employment.
- Retirement: Travelers matches your 401(k) contributions dollar-for-dollar up to your first 5% of eligible pay, subject to an annual maximum. If you have student loan debt, you can enroll in the Paying it Forward Savings Program. When you make a payment toward your student loan, Travelers will make an annual contribution into your 401(k) account. You are also eligible for a Pension Plan that is 100% funded by Travelers.
- Paid Time Off: Start your career at Travelers with a minimum of 20 days Paid Time Off annually, plus nine paid company Holidays.
- Wellness Program: The Travelers wellness program is comprised of tools, discounts and resources that empower you to achieve your wellness goals and caregiving needs. In addition, our mental health program provides access to free professional counseling services, health coaching and other resources to support your daily life needs.
- Volunteer Encouragement: We have a deep commitment to the communities we serve and encourage our employees to get involved. Travelers has a Matching Gift and Volunteer Rewards program that enables you to give back to the charity of your choice.
Employment Practices
Travelers is an equal opportunity employer. We value the unique abilities and talents each individual brings to our organization and recognize that we benefit in numerous ways from our differences.
In accordance with local law, candidates seeking employment in Colorado are not required to disclose dates of attendance at or graduation from educational institutions.
If you are a candidate and have specific questions regarding the physical requirements of this role, please send us an email so we may assist you.
Travelers reserves the right to fill this position at a level above or below the level included in this posting.
To learn more about our comprehensive benefit programs please visit http://careers.travelers.com/life-at-travelers/benefits/.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 33,423 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Travelers, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 202 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. Disclosed range: $153K to $253K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Travelers AI Hiring
Travelers has 1 open AI role right now. They're hiring across Data Engineer. Based in Hartford, CT, US. Compensation range: $253K - $253K.
Location Context
Across all AI roles, 7% (2,320 positions) offer remote work, while 30,984 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 33,423 open positions tracked in our dataset. By seniority: 3,283 entry-level, 20,769 mid-level, 6,381 senior, and 2,990 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,320 positions). The remaining 30,984 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 33,423 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (30,275), AI Software Engineer (749), AI Product Manager (741). 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 (3,283) are outnumbered by mid-level (20,769) and senior (6,381) 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 2,990 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,320 positions), with 30,984 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (21,235 postings), Aws (11,126 postings), Rust (9,803 postings), Python (4,999 postings), Azure (3,220 postings), Gcp (2,707 postings), Prompt Engineering (1,817 postings), Openai (1,487 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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