Temporary AI & Machine Learning Engineer
J
JobgetherHealthcare Analytics
Based in United StatesPart-TimeSenior
Salary79.38 - 150.55 USD per hour
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Job Details
- Experience
- 10+ years of experience in health risk adjustment and related healthcare analytics domains. 5+ years of experience designing and building production predictive models.
- Required Skills
- Cloud ComputingMachine LearningSoftware EngineeringLLM
Requirements
- Fellow of the Society of Actuaries (FSA) designation required.
- Master of Public Health (MPH) degree with experience in healthcare insurance and provider markets preferred.
- 10+ years of experience in health risk adjustment and related healthcare analytics domains.
- 5+ years of experience designing and building production predictive models.
- Experience developing and deploying AI solutions in cloud-based production environments.
- Strong software engineering capabilities and experience building scalable AI-enabled applications.
- Deep knowledge of statistics, machine learning concepts, and predictive modeling techniques.
- Experience building applications using large language models such as GPT, Claude, Llama, Gemini, or similar technologies.
- Ability to evaluate, prototype, and implement rapidly evolving AI technologies.
- Excellent communication skills with the ability to explain complex technical concepts clearly.
- Self-directed, collaborative, curious, and adaptable mindset with strong problem-solving abilities.
Responsibilities
- Design, architect, test, and deploy production-grade AI and machine learning solutions for high-impact business use cases.
- Build reusable data pipelines, modeling infrastructure, and AI frameworks that enable faster development and deployment of predictive solutions.
- Develop agentic AI workflows that automate established modeling processes and extend analytical capabilities to new prediction challenges.
- Create applications powered by large language models, retrieval-augmented generation (RAG) systems, and AI agents.
- Evaluate AI technologies, foundation models, and tools to identify the best solutions while considering security, reliability, performance, and cost requirements.
- Develop AI services and APIs that integrate with existing software products and operational workflows.
- Establish strong software engineering practices for AI systems, including scalability, testing, maintainability, and deployment automation.
- Prototype emerging AI concepts and transform successful experiments into production-ready capabilities.
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