3 or more years relevant Machine Learning Engineer Experience.
Proven experience with : Artificial intelligence and machine learning platforms (e.g., AWS, Azure or GCP). Containerization technologies (e.g., Docker) or containerorchestration platforms (e.g., Kubernetes). CI / CD tools (e.g., Github Actions). Programming languages andframeworks (e.g., Python, R, SQL). MLOps engineering principles, agile methodologies, and DevOps life-cycle management. Technical writing and documentation for AI / ML models and processes. Healthcare data andmachine learning use cases.
Healthcare Expertise : Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems.
REQUIRED qualifications :
Experience in managing end-to-end ML lifecycle.
Experience in managing automation with Terraform.
Containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes).
CI / CD tools (e.g., Github Actions).
Programming languages and frameworks (e.g., Python, R, SQL).
Deep understanding of coding, architecture, and deployment processes
Strong understanding of critical performance metrics.
Extensive experience in predictive modeling, LLMs, and NLP
Exhibit the ability to effectively articulate the advantages and applications of the RAG framework with LLMs
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Machine Learning • Los Angeles, CA, United States
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