Staff AI Machine Learning Engineer

The Hartford•Hartford, CT

21h•$125,760 - $188,640

About The Position

The Hartford is seeking a Staff AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Middle and Large Business Applied AI team. The Hartford is developing industry-leading AI and machine learning capabilities to improve the various facets of the Middle and Large Business underwriting experience. On the Middle and Large 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 Staff Machine Learning AI Engineer, you will play a critical role in designing, building, and operationalizing production-grade AI and ML 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, and delivering products with a full monitoring solution to ensure they continue to deliver as expected. We earn the right to influence with humble confidence, listening carefully to learn from our customers and becoming partners in problem solving. We are practical and evolutional, 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.
Bachelor’s degree in related field or 7+ years of equivalent experience in a Research, Engineering, Data Science, or DevOps Role.
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 pipelines.
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.
Candidate must be authorized to work in the US without company sponsorship.
The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Responsibilities

Research, experiment with, and implement suitable Generative AI 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.