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.
Minimum 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
Preferred Skills
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.
Requirements
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
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).
Candidates must be authorized to work in the US withou
Additional Information
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 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.
Our core values
We build AI solutions, not models. We are thoughtful in supporting the end-to-end business problem, with an eye to systems design.
We are trusted and transparent. We collaborate tightly with our partners and are mindful of their capacity to absorb change.
We provide assets that are safe to buy. Our products are delivered with a full monitoring solution to ensure our products continue to deliver as expected.
We will earn the right to influence. With humble confidence, we listen carefully to learn from our customers and become partners in problem solving.
We are practical and evolutional. We first deliver a minimally viable product and over time expand its sophistication based on feedback.
Thehartford · Chicago, IL