AI/ML Engineer focusing on machine learning applications in oncology. Developing analytical solutions to enhance patient selection and drive next - generation assets in healthcare.
About the role
- Senior Machine Learning Engineer at The Hartford building and scaling AI/ML solutions. Collaborating with data science teams to deploy models in production environments.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- Health insurance
- 401(k)
- Flexible work arrangements
- Professional development opportunities
- Bonuses
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