Machine Learning Engineer

Title: Machine Learning Operations Engineer

Location: Los Angeles, CA 90032

Duration: 12 Months (Possible Extension)

Note: Hiring W2 only

Summary:

Technical Expertise:
• Proficiency in developing end-to-end scalable ML infrastructures using on-premise or cloud platforms such as AWS, GCP, or Azure.
• Strong skills in creating and optimizing CI/CD pipelines for machine learning models, including automating testing and deployment processes.
• Experience in developing AI pipelines for data ingestion, preprocessing, search, and retrieval.
• Competence in setting up monitoring and logging solutions for tracking model performance, system health, and anomalies.
• Familiarity with version control systems for tracking changes in ML models and associated code.
• Understanding of security and compliance standards related to machine learning systems, including data protection and privacy regulations.

Leadership and Collaboration:
• Ability to lead engineering efforts in ML/GenAI model development, LLM advancements, and optimizing deployment frameworks aligned with business strategies.
• Demonstrated ability to collaborate with cross-functional teams, including data scientists, data engineers, analytics teams, and DevOps teams.

Documentation and Process Management:
• Skilled in maintaining clear and comprehensive documentation of ML Ops processes, workflows, and configurations.

Preferred Qualifications:
• Proficiency in containerization technologies such as Docker and Kubernetes.
• Knowledge of healthcare standards, regulations, and systems, including integrating ML models with Electronic Health Records (EHR) systems.
• Certifications in machine learning or related fields.

Additional 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.

Education:
• Bachelor’s degree in computer science, artificial intelligence, informatics, or a closely related field.
• Master’s degree is a plus.

Experience:
• At least 3 years of relevant experience as a Machine Learning Engineer.
• Proven experience in deploying and maintaining production-grade machine learning models, ensuring real-time inference, scalability, and reliability