Senior AI & MLOps Engineer - Cloud Data Science

The Hartford; Chicago, Northern (IL, KY); Hybrid; USD 137,000 - 206,000; Full time

Job summary

The Hartford seeks a Senior AI Machine Learning Engineer to build and operationalize MLOps services for the Customer Operations Data Science team. You will work with product, engineering, and operations to deliver production‑grade AI solutions that improve customer interactions across omnichannel journeys.

We emphasize end-to-end system design, monitoring, and collaboration with Enterprise Data and Cloud Enablement teams, offering a hybrid work model across multiple office locations in the US.

Qualifications

• 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 as generative AI, Agentic workflows, and embedding LLMs into automated processes.

Responsibilities

• Research, experiment with, and implement Generative and ML algorithms, tools and technologies.
• Identify opportunities such as new data sources and analytical techniques to maintain competitive advantage.
• Review work with leadership to calibrate deliverables against expectations.
• Design, develop and maintain Models as Service.
• Mentor junior engineers and present concepts to technical audiences.

Skills

Python development

ML engineering

CI/CD pipelines

IAC

AWS

GCP

SageMaker

Docker

Kubernetes

Airflow

Data pipelines

Big data

Tools

GitHub Actions

Terraform

CloudFormation

Spark

Redshift

Snowflake

BigQuery

Job description

The Hartford seeks a Senior AI Machine Learning Engineer to build and operationalize MLOps services for the Customer Operations Data Science team. You will work with product, engineering, and operations to deliver production‑grade AI solutions that improve customer interactions across omnichannel journeys.

We emphasize end-to-end system design, monitoring, and collaboration with Enterprise Data and Cloud Enablement teams, offering a hybrid work model across multiple office locations in the US.