Machine Learning Engineer

Machine Learning Engineer in Los Angeles, CA. Opportunity!

This Jobot Consulting Job is hosted by : Robert Reyes

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Salary : $120,000 - $180,000 per year

A bit about us :

Prestige Hospital System

Placed #1 in both California in a broad assessment of excellence in hospital-based patient care.

Why join us?

Competitive Salary$$

Stellar Benefits (Medical, Dental Vision, Life Insurance)

Flexible Schedule

Job Stability

Career growth

The position offers a competitive salary

If you are passionate, thrive in a fast-paced environment and are ready to take your career to the next level, we would love to hear from you.

Job Details

Job Details :

We are seeking a dynamic and innovative Consulting Machine Learning Engineer to join our Tech Services team. The successful candidate will be responsible for the full lifecycle management of machine learning models, including design, build, and maintenance. As an MLOps Engineer, you will play an integral role in implementing artificial intelligence solutions across our organization. You will partner with data scientists, data team members, and clinical operations to deploy, monitor, and maintain machine learning solutions that will improve operational efficiency and advance research.

Responsibilities :

• Deploying and maintaining production-grade machine learning models, ensuring real-time inference, scalability, and reliability.
• Developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as AWS, GCP, or Azure.
• Leading engineering efforts in creating and implementing methods and workflows for ML / GenAI model engineering, LLM advancements, and optimizing deployment frameworks.
• Developing AI pipelines for various data processing needs, ensuring solutions meet all technical and business requirements.
• Collaborating with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines.
• Implementing and optimizing CI / CD pipelines for machine learning models, automating testing and deployment processes.
• Setting up monitoring and logging solutions to track model performance, system health, and anomalies.
• Implementing version control systems for machine learning models and associated code.
• Ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.
• Maintaining clear and comprehensive documentation of ML Ops processes and configurations.

Qualifications :

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