Senior Machine Learning Engineer

$113,360 - $170,040/Yr

The Hartford - Hartford, CT

Full-time - Mid Level

Hartford, CT

10,001+ employees

Insurance Carriers and Related Activities

About the position

The Hartford's Personal Lines Data Analytics team is seeking a Senior Machine Learning Engineer to develop Machine Learning Operations (MLOps) and Generative AI (GenAI) services. This role is pivotal in delivering modern data science products that drive meaningful outcomes for the enterprise. The position offers a hybrid or remote work arrangement, with expectations for in-office work for local candidates three days a week.

Responsibilities

• Research, experiment with, and implement suitable frameworks, tools, and technologies for AI/ML decision making at scale.
• Identify and assess opportunities for new data sources and analytical techniques to maintain competitive advantage.
• Review work with leadership and partners to calibrate deliverables against expectations.
• Own the design, development, and maintenance of MLOps and GenAI platforms and services.
• Mentor junior engineers and provide thought leadership.
• Collaborate with Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
• Deliver critical milestones for model deployment in the AWS cloud.
• Promote MLOps best practices within the Data Science community.
• Present new developments and innovations in data and analytics forums.

Requirements

• Must be authorized to work in the U.S. now and in the future.
• Master's degree in a related field or 5+ years of equivalent experience in a research function.
• Strong understanding of Data Structures and algorithms.
• Experience building and deploying WebServices using RESTful API in a Cloud environment.
• Experience building CICD pipelines using Jenkins or equivalent.
• Strong application development experience using Python or Java in AWS or other public cloud environments.
• Familiarity with big data technologies (Hadoop, Spark, Hive, etc.) and RDBMS.
• Good understanding of Generative AI technologies, frameworks, key LLMs, and architecture patterns.
• Experience in end-to-end model development lifecycle, from ideation through post-production monitoring.
• Experience with Solution Design and Architecture of data and ML pipelines, integrating with Enterprise systems.
• Good understanding of Data Science model development lifecycle including model training, deployment, log management, and monitoring.

Nice-to-haves

• Development experience for WebService API with AWS suite of Tools.
• Familiarity with Sage Maker, Python Flask, or Spring Boot.
• Experience working with VMs, Docker, Kubernetes, and EC2 environment.
• Basic understanding of ML frameworks (Tensorflow, Anaconda, Scikit Learn, H20).
• Familiarity with credentials management using vault services and protecting APIs using tokens.
• Experience with Infrastructure as Code (IaaC) templates (Cloud Formation or Terraform).
• Experience with Agile framework and scrum/Kanban based project management.

Benefits

• Short-term or annual bonuses
• Long-term incentives
• On-the-spot recognition