Machine Learning Engineer
Machine Learning Engineer
Location: Los Angeles, CA (Open to Remote – MUST work PST hours)
Duration: 12-month W2 contract (with a possibility of extension)
Pay: $84-86 / HR
Work Hours: 8 AM - 5 PM
About the Role
On behalf of our private university client in Los Angeles, CA, we are seeking a Machine Learning Engineer to join our team and play a key role in deploying and maintaining production-grade ML models. In this role, you will be responsible for building scalable end-to-end ML infrastructures, optimizing CI/CD pipelines, and ensuring real-time inference, scalability, and reliability.
If you have a strong background in ML model development, cloud technologies, and MLOps, we encourage you to apply!
Responsibilities:
• Design and develop end-to-end scalable ML infrastructures on AWS, GCP, or Azure.
• Implement and optimize CI/CD pipelines for ML models, automating testing and deployment.
• Build and manage AI pipelines for data ingestion, preprocessing, search, and retrieval.
• Set up monitoring and logging solutions to track model performance, system health, and anomalies.
• Maintain version control systems for tracking ML model changes.
• Ensure security and compliance with data protection and privacy regulations.
• Lead efforts in ML/GenAI model development and LLM advancements aligned with business needs.
• Collaborate with data scientists, data engineers, analytics teams, and DevOps to optimize ML solutions.
• Maintain clear and comprehensive documentation of ML processes and workflows.
Qualifications:
• Bachelor’s degree in Computer Science, Artificial Intelligence, Informatics, or a related field (Master’s is a plus).
• At least 3 years of experience as a Machine Learning Engineer.
• Proven expertise in deploying and maintaining production-grade ML models.
• Strong experience with cloud platforms (AWS, GCP, Azure).
• Proficiency in CI/CD pipeline development for ML models.
• Understanding of AI pipeline development (data ingestion, preprocessing, retrieval).
• Experience with monitoring and logging solutions for ML models.
• Familiarity with version control systems (e.g., Git) for ML model tracking.
• Knowledge of security and compliance standards in ML systems.
Preferred:
• Experience with Docker, Kubernetes, and containerization.
• Knowledge of healthcare standards and EHR integration with ML models.
• Certifications in Machine Learning or related fields.
Please submit your resume in Word or PDF format to be considered.
