AI Machine Learning Engineer

Sourced from glassdoor.com

Build MLOps services for the Global Specialty Applied AI team at The Hartford. Research and implement generative and traditional ML algorithms, and deploy production-grade AI solutions in AWS and GCP cloud environments. Collaborate closely with product, engineering, and platform leaders to deliver measurable impact.

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

• 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. 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 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 LLMs into automated processes.