Case Status®

Machine Learning Engineer

• Translate product problems into ML approaches, baseline against simple methods, and define success metrics and evaluation protocols.
• Build and maintain training datasets.
• Iterate on models, then graduate them from notebooks to production-quality services with proper testing, logging, and observability.
• Design, deploy, and maintain inference services and batch jobs. Handle scale, latency, and cost with appropriate architectures, queues, and caching.
• Monitor for data drift, model degradation, and operational issues. Set up dashboards and alerts, run A/B or offline evaluations.
• Work across the stack as needed: schema design, APIs, CI/CD, containerization, and infrastructure-as-code in cloud environments.
• Contribute to agentic AI development: implement orchestration layers where AI agents can plan, act, and use tools, and integrate these agents with Case Status’ platform using MCP.
• Document decisions and results, and communicate trade-offs to technical and non-technical stakeholders.