Senior AI Machine Learning Engineer
About The Position
The Hartford seeks a driven, team-focused Senior AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Customer Operations Data Science team. The Hartford is developing industry‑leading AI and machine learning capabilities to improve customer experience (CX) at scale. Within Customer Operations Data Science, we build modern AI products that optimize customer interactions across omnichannel journeys, supporting operational areas such as the Contact Center, Premium Audit, and Billing. As a Senior Machine Learning Engineer, you will play a critical role in designing, building, and operationalizing production‑grade AI solutions—partnering closely with product, engineering, and operations leaders to deliver measurable impact.
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 using both the AWS and GCP suite of tools.
- Familiarity with SageMaker, Streamlit, web security, credentials and API management tools
- Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
- Experience building and deploying webservices in a cloud environment.
- Experience building CICD pipeline using Jenkins or equivalent
- Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar
- Expert-level Github experience, including Github Actions
- Strong object oriented development experience using Python, Java, C#
- Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery
- Experience in end to end model development lifecycle, from ideation through post production monitoring.
- Experience with workflow automation platforms (Apache Airflow, Autosys, similar)
- Experience with Solution Design and Architecture of data pipelines
- Basic understanding of Data Science model development life cycle
- 4+ years of ML engineering, data manipulation and application development
- 4+ years Python development experience
- 4+ years working with IAC, developing CICD pipelines
- 1+ years of experience in the insurance or broader financial services industry
- 1+ years SQL development experience
- Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes
Nice To Haves
- Fundamentally strong with Data Structures and algorithms.
- Experience working with Docker, Kubernetes and EC2 environment.
- Experience building ML and data pipeline and orchestration services
- Basic understanding of ML frameworks i.e. Tensorflow, Anacoda, Scikit Learn,
- Experience working in an Agile framework.
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 design, development and maintenance of Models as Service
- 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, Data Science, 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.
Benefits
- short-term or annual bonuses
- long-term incentives
- on-the-spot recognition
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What This Job Offers
Job Type
Full-time
Career Level
Senior
