Sr Machine Learning Engineer

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's Personal Lines Data Analytics team seeks energetic and passionate Senior Machine Learning Engineer to help build and scale our next-generation Machine Learning Operations (MLOps) & Generative AI (GenAI) platforms. This role blends software engineering, DevOps, and machine learning expertise to deliver robust, scalable, and secure AI/ML solutions. You will be instrumental in enabling our data science teams to deploy models efficiently and responsibly in production environments.

• We build artificial intelligence/machine learning solutions, not models. We support end-to-end business problems with a focus on systems design.
• We are trusted and transparent, collaborating closely with our partners and considering their capacity for change.
• Our products are delivered with full monitoring solutions to ensure they continue to perform as expected.
• We listen carefully to our customers and become partners in problem-solving with humble confidence.
• We deliver minimally viable products first and expand their sophistication over time based on feedback.

• Research, experiment with, and implement suitable frameworks, tools, and technologies to enable AI/ML decision-making at scale.
• Participate in identifying and assessing opportunities, such as the value of new data sources and analytical techniques, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for the ownership of design, development, and maintenance of MLOps and GenAI platforms and services.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
• Delivery of critical milestones for model deployment in the Google Cloud Platform (GCP) and AWS cloud.
• Develop, adopt, and promote MLOps best practices to the Data Science community.
• Implement infrastructure-as-code using Terraform or CloudFormation to automate deployments.
• Contribute to the development of agentic AI capabilities and support experimentation with LLMs and GenAI frameworks.

• Must be authorized to work in the U.S. now and in the future.
• Bachelor's degree in related field and 5+ years of experience.
• Solid understanding of ML lifecycle: model training, deployment, monitoring, and feedback loops.
• Strong application development experience using Python.
• 3+ years of hands-on experience developing with one of the public clouds including tools and techniques to auto scale systems.
• Experience with CI/CD and IAC tools (e.g., terraform, Jenkins, GitHub Actions) and containerization (Docker, Kubernetes).
• Good understanding of Generative AI technologies, frameworks, key LLMs, and architecture patterns.
• Exposure to agentic AI architectures and prompt engineering.
• Good understanding and experience building orchestration framework for real-time and batch model services.
• Good understanding of various model development algorithms and types of ML use cases e.g., regression, classification, etc.
• Strong fundamental knowledge of data structures and algorithms

• Development experience for WebService API with AWS suite of Tools.
• Familiarity with big data technologies (i.e., Hadoop, Spark, Hive, etc.) and RDBMS.
• Hands-on experience with public cloud GCP, especially Vertex AI, Cloud Run, BigQuery, and GKE.
• Basic understanding of ML frameworks i.e., Tensorflow, Scikit Learn, etc.
• Experience with Agile framework and scrum/Kanban based project management.