MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Machine Learning Engineer developing machine learning models and workflows for the insurance industry at Federato. Focused on delivering robust and reliable ML solutions to improve underwriting processes.
Responsibilities
- Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs
- Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers
- Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks
- Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions
- Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions
- Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution
- Take end-to-end ownership of problems — implementing fixes or coordinating with engineering and infrastructure teams as needed
- Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions
Requirements
- Bachelor's or master’s degree in Mathematics, Operations Research, Data Science, Artificial Intelligence, or a related field with foundational knowledge in machine learning, deep learning, and natural language processing.
- Experience working in a fast-paced, cross-functional environment
- 2+ years of experience as a Machine Learning Engineer, Applied Scientist, or similar role delivering ML solutions in production
- Experience working directly with customers or stakeholders to translate business needs into technical solutions
- Hands-on experience adapting, extending, and deploying ML/LLM systems (including agentic workflows and prompt engineering) in real-world use cases
- Strong experience with experimentation, evaluation, and monitoring pipelines, including analyzing production logs and debugging systems
- Experience deploying and iterating on ML systems in cloud environments in collaboration with engineering teams
- Proven track record of ownership — driving issues through to resolution in production systems
Benefits
- Total compensation package does include stock options, benefits and additional perks
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