AI Machine Learning Engineer
About The Position
The Hartford is seeking an AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industry-leading AI and machine learning capabilities to improve the various facets of the Global Specialty underwriting experience. On the Global Specialty Applied AI team, we utilize the latest AI products and frameworks to accelerate the processes that our partners touch day to day and advance the speed and intelligence with which we make our decisions. As a Machine Learning AI Engineer, you will play a key role in contributing to the designing, building, and operationalizing production-grade AI solutions—partnering closely with product, engineering, and platform leaders to deliver measurable impact. Our core values include building AI solutions with a focus on the end-to-end business problem and systems design, being trusted and transparent through close collaboration, providing safe and monitored assets, earning the right to influence through listening and problem-solving, and being practical and evolutional by delivering minimally viable products and expanding based on feedback.
Requirements
- Must be authorized to work in the U.S. now and in the future.
- 1+ years of equivalent experience in a research or DevOps function.
- Development experience developing solutions within AWS, GCP or both.
- Exposure to developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
- Familiarity with building and deploying API services within the Cloud.
- Familiarity building CICD pipelines using Jenkins or equivalent.
- Exposure with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents.
- Experience in Unix, git, and strong object oriented development experience using Python.
- Exposure to with workflow automation platforms (Apache Airflow, Autosys, similar).
- Basic understanding of Data Science model development life cycle.
- Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes.
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.
- Accountable for deployment design, development and maintenance of both traditional ML and AI models.
- Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams.
- Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.
- 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
Mid Level
Education Level
No Education Listed
