A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
Health Care Background Prefered Position- Sr/Lead AI/ML Engineer & Sr Data Scientist Location- Minnetonka, Minnesota ( Onsite from Day 1 )
Duration :
12
Months JD:
We are seeking an innovative and technically strong AI / Automation / Platform Engineer to design, develop, and maintain scalable AI-powered solutions, automation frameworks , and enterprise platforms. The ideal candidate will have experience in AI technologies, workflow automation , cloud platforms, system integrations , and modern software engineering practices . This role requires collaboration with business stakeholders, product teams, and technical teams to drive digital transformation initiatives and improve operational efficiency. We are seeking a Senior AI Software Engineer to design, build, and scale our next-generation AI agents, applications, and platform capabilities. You own complex AI solutions from technical design through production operation and help establish the engineering patterns that enable teams across Medica to build with AI safely, reliably, and quickly. You combine strong software engineering fundamentals with hands-on experience using modern AI models, agent architectures, cloud infrastructure, and developer tooling. You operate with high autonomy, turn ambiguous business and technical problems into practical solutions, and make sound architectural tradeoffs. This role is expected to have an informed, opinionated perspective on how AI changes software engineering and how work gets done. You actively experiment with emerging technologies while maintaining a strong bias toward production reliability, measurable outcomes, security, maintainability, and appropriate use of AI in healthcare. Key accountability Desired key actions to successfully achieve key role accountabilities End to End AI Platform Development Design, implement, deploy, and operate AI pipelines and systems. Own evaluation setup, benchmarking, and the integration of components into products. Build reliable, secure, and observable production systems. AI Use Case Development Prototype, build, and productionize AI-powered features. Lead the technical solutioning of healthcare and enterprise challenges using modern AI tools and frameworks. Technical Collaboration & Code Quality Lead architectural discussions, code reviews, and pair programming. Establish engineering best practices and deliver clean, maintainable code.
