Sr Data Engineer - GE07BE
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 a Machine Learning Engineer within Middle and Large Data Science, AI, and Analytics to design, develop, and implement modern and sustainable data assets to fuel machine learning and artificial intelligence solutions across a wide range of strategic initiatives.
The Middle & Large Business Data Science, AI, and Analytics team is one of three business-aligned teams in the broader Business Insurance Data Science, AI, and Analytics organization. Our team has cultivated a reputation for driving cross-functional, multi-faceted solutions for not only our Middle and Large Business partners, but the enterprise as a whole. Our portfolio ranges from real-time modeling capabilities that deliver actionable insights for our partners for all phases of the policy lifecycle to cutting-edge generative and agentic AI solutions that bridge the accessibility gap across data and platform to ETL pipelines that traverse all manner of databases and data formats.
As a Machine Learning Engineer, you will participate in the entire model development lifecycle process in support of the data, AI, and modeling solutions built by the team, while growing your knowledge of emerging technologies. We use the latest data technologies, software engineering practices, ML/AIOps, Agile delivery frameworks, and are passionate about building well-architected and innovative solutions that drive business value. This cutting edge and forward focused organization presents the opportunity for collaboration, self-organization within the team, and visibility as we focus on continuous business data and analytics delivery.
This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).

SKILLS

• Experience with ML/AIOps tools and practices in an enterprise cloud environment, including WebServices using RESTful API, CI/CD pipeline using Jenkins or equivalent, cloud resource deployments, identity and access management, automated testing, and model monitoring
• Strong application development experience using Python in AWS, GCP or other enterprise cloud environments
• Experience with working with IaC solutions such as CloudFormation or Terraform.
• Familiarity with machine learning algorithms, metrics and the end-to-end AI/ML development lifecycle, from ideation through post-production monitoring
• Strong understanding of data structures, big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS
• Experience with Solution Design and Architecture of data and ML pipelines as well as integrating with enterprise systems
• Experience building orchestration frameworks for real-time, streaming and batch, and asynchronous and synchronous services in cloud environment
• Experience with Agile methodologies
• Experience with version control such as git
• Familiarity with generative and agentic AI technologies and tools
• Some experience in guiding and mentoring junior engineers
• Proficiency in cloud platforms; AWS Cloud or GCP experience preferred

• Design, develop, maintain, and optimize scalable, efficient, and secure machine learning pipelines in production
• Implement and promote ML/AIOps practices to automate and streamline the deployment, monitoring, and maintenance of machine learning models
• Consult with data scientists and data engineers to optimize model structure and feature engineering for production
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations
• Conduct code reviews and provide mentorship to junior engineers
• Collaborate with AI/ML platform teams and architects for direct contribution to the design, development, and maintenance of machine learning model delivery frameworks (real-time or batch), architecture, and best practices
• Stay up-to-date with the latest advancements in machine learning, ML/AIOps, and related technologies