Sr AI Machine Learning Engineer

The Hartford•Columbus, OH

19h

About The Position

The Hartford is seeking a Senior 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 Senior Machine Learning AI Engineer, you will play a critical role in designing, building, and operationalizing production-grade AI solutions—partnering closely with product, engineering, and platform leaders to deliver measurable impact. Our core values include building AI solutions, not just models, being thoughtful in supporting the end-to-end business problem with an eye to systems design, being trusted and transparent, collaborating tightly with partners and being mindful of their capacity to absorb change, providing assets that are safe to buy, delivering products with a full monitoring solution, earning the right to influence, listening carefully to learn from customers and becoming partners in problem solving, and being practical and evolutional by first delivering a minimally viable product and over time expanding its sophistication based on feedback.

Requirements

Must be authorized to work in the U.S. now and in the future.
Master’s degree in related field or 5+ years of equivalent experience in a research or DevOps function.
Development experience developing solutions within AWS, GCP or both.
Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
Experience building and deploying API services within the Cloud.
Experience building CICD pipeline using Jenkins or equivalent
Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents
Experience in Unix, git, and strong object oriented development experience using Python
Experience in end to end model development lifecycle, from ideation through post production monitoring.
Experience 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.
Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
Accountable for deployment design, development and maintenance of both traditional ML and AI models.
Work with junior engineers and peers to provide mentorship and thought leadership.
Be comfortable presenting new concepts to technical audiences.
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 clouds.
Adopt and promote MLOps best practices to the Data Science community.