Jobs via Dice ยท 3 hours ago
Machine Learning Operations Engineer
Los Angeles, CA
Full-time
Onsite
Mid Level
3+ years expDice is the leading career destination for tech experts at every stage of their careers. Our client, Compunnel Inc., is seeking a Machine Learning Operations Engineer responsible for the full lifecycle management of machine learning models and implementing AI solutions. The role involves collaborating with various teams to ensure seamless integration and automation of AI systems in production environments.
Computer Software
Responsibilities
Design, build, deploy, and maintain machine learning models across production environments
Partner with data scientists, engineers, and clinical operations to implement AI solutions
Develop and continuously improve MLOps pipelines for monitoring, versioning, and deployment
Implement best practices for testing, debugging, and performance monitoring of AI systems
Ensure seamless integration, automation, and scaling of AI solutions within existing infrastructure
Support predictive modeling, large language models (LLMs), and natural language processing (NLP) initiatives
Apply Retrieval-Augmented Generation (RAG) frameworks with LLMs and articulate their advantages
Lead engineering efforts in ML/GenAI model workflows and deployment frameworks
Develop AI pipelines for data ingestion, preprocessing, and retrieval to meet technical and business requirements
Implement CI/CD pipelines for machine learning models, automating testing and deployment
Establish monitoring and logging solutions to track model performance and system health
Apply version control systems for ML models and associated code
Ensure compliance with healthcare regulations, data protection, and privacy standards
Maintain clear and comprehensive documentation of MLOps processes and configurations
Qualification
Required
Bachelors degree in computer science, artificial intelligence, informatics, or related field
Minimum of 3 years of relevant machine learning engineering experience
Experience managing end-to-end ML lifecycle
Proficiency with automation tools such as Terraform
Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes)
Experience with CI/CD tools (e.g., GitHub Actions)
Strong programming skills in Python, R, and SQL
Deep understanding of coding, architecture, and deployment processes
Strong knowledge of critical performance metrics for ML systems
Extensive experience in predictive modeling, LLMs, and NLP
Familiarity with healthcare regulations, standards, and EHR systems integration
Preferred
Masters degree in computer science, engineering, or related field
Experience with cloud platforms (AWS, Azure, Google Cloud Platform)
Background in healthcare data and machine learning use cases
Technical writing and documentation experience for AI/ML models and processes
Certifications in cloud platforms, DevOps, or machine learning are a plus
Company
Jobs via Dice
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0-1 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase