AI Machine Learning Engineer

The Hartford•Hartford, CT

5h•Hybrid

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industry‑leading AI and machine learning capabilities to improve the various facets of the Global Specialty underwriting experience. On the Global Specialty Applied AI team, we utilize the latest AI products and frameworks to accelerate the processes that our partners touch day to day and advance the speed and intelligence with which we make our decisions. As a Machine Learning AI Engineer, you will play a key role in contributing to the designing, building, and operationalizing production‑grade AI solutions—partnering closely with product, engineering, and platform leaders to deliver measurable impact. Our core values We build AI solutions, not models. We are thoughtful in supporting the end-to-end business problem, with an eye to systems design. We are trusted and transparent. We collaborate tightly with our partners and are mindful of their capacity to absorb change. We provide assets that are safe to buy. Our products are delivered with a full monitoring solution to ensure our products continue to deliver as expected. We will earn the right to influence. With humble confidence, we listen carefully to learn from our customers and become partners in problem solving. We are practical and evolutional. We first deliver a minimally viable product and over time expand its sophistication based on feedback.

Requirements

Must be authorized to work in the U.S. now and in the future.
1+ years of equivalent experience in a research or DevOps function.
Development experience developing solutions within AWS, GCP or both.
Exposure to developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
Familiarity with building and deploying API services within the Cloud.
Familiarity building CICD pipelines using Jenkins or equivalent
Exposure with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents
Experience in Unix, git, and strong object oriented development experience using Python
Exposure to with workflow automation platforms (Apache Airflow, Autosys, similar)
Basic understanding of Data Science model development life cycle
Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes

Responsibilities

Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
Accountable for deployment design, development and maintenance of both traditional ML and AI models.
Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.
Adopt and promote MLOps best practices to the Data Science community.