Machine Learning Engineer

Kforce Inc is seeking a Machine Learning Engineer to join their team in Los Angeles, CA. The role involves managing the end-to-end machine learning lifecycle, developing scalable ML infrastructures, and deploying production-grade machine learning models, particularly in the healthcare sector.

Responsibilities:

• 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
• Proven experience with: Artificial intelligence and machine learning platforms (e.g., AWS, Azure or GCP); 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). MLOps engineering principles, agile methodologies, and DevOps life-cycle management; Technical writing and documentation for AI/ML models and processes; Healthcare data and machine 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
• Production Deployment and Model Engineering: Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
• Scalable ML Infrastructures: Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure

Requirements:

• Bachelor's degree in Computer Science, Artificial Intelligence, Informatics or closely related field; Master's degree in computer science, engineering or closely related field preferred
• 3 or more years relevant Machine Learning Engineer Experience
• 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
• Proven experience with: Artificial intelligence and machine learning platforms (e.g., AWS, Azure or GCP); 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). MLOps engineering principles, agile methodologies, and DevOps life-cycle management; Technical writing and documentation for AI/ML models and processes; Healthcare data and machine 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
• Production Deployment and Model Engineering: Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
• Scalable ML Infrastructures: Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure