Machine Learning Operations Engineer
Machine Learning Operations Engineer
Location: Los Angeles, CA (Open to Remote – MUST work PST hours)
Duration: 12-month contract (with a possibility of extension)
Pay: $80 / 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.
- Experience in managing end-to-end ML lifecycle.
- Strong experience with cloud platforms (AWS, GCP, Azure).
- 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.
Required Experience:
- 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.
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.