Machine Learning Engineer (MLOps / AI Infrastructure)

Preference: Southern California but will take candidates located in PDT, MST, and CST \| No East Coast candidates so no EST.

TITLE: Machine Learning Engineer (MLOps / AI Infrastructure)

MUST HAVE HEALTHCARE PROVIDER EXPERIENCE, do not submit yourself without it

Industry Experience (Important)

• Experience working with

healthcare data

• Familiarity with:
• EHR systems
• data privacy / compliance standards

SALARY RANGE: $152,000 – $180,000 Base (FTE, Exempt)

We’re partnered with a

leading healthcare organization at the forefront of AI-driven patient care, clinical research, and operational innovation

. This team is actively building and scaling

production-grade AI systems

that directly impact patient outcomes and healthcare delivery.

This is a

high-priority, high-visibility role

where you’ll help design and deploy real-world AI solutions in a mission-critical environment.

Read This First (Role Clarity)

This is

NOT a pure Machine Learning / Data Science role.

• This is an

MLOps / ML Infrastructure Engineering role

• Focus \=

deployment, scalability, automation, and production systems

If your experience is primarily:

• building models
• experimenting in notebooks
• tuning algorithms

This will

NOT

be the right fit.

What You’ll Do

• Own the

end-to-end ML lifecycle

(build → deploy → monitor → scale)

• Design and maintain

production-grade ML systems with real-time inference

• Build and optimize

CI/CD pipelines for machine learning workflows

• Develop

scalable ML infrastructure

in cloud environments (AWS, Azure, or GCP)

• Implement

monitoring, logging, and performance tracking

for deployed models

• Partner cross-functionally with

data scientists, engineers, and clinical teams

• Support

GenAI/LLM deployments

, including RAG-based systems

Required Technical Experience

• 3\+ years in

Machine Learning Engineering / MLOps / ML Infrastructure

• Proven experience managing

end-to-end ML lifecycle in production

• Strong hands-on experience with:

Python

(will be tested live in interview)

Docker \+ Kubernetes

Terraform (required)

CI/CD tools (GitHub Actions preferred)

• Experience with

cloud platforms

(AWS, Azure, or GCP)

• Deep understanding of:

System architecture

Deployment pipelines

Performance optimization

• Experience with:

LLMs, NLP, and predictive modeling

RAG frameworks (must be able to explain and implement)

• Industry Experience (Important), Experience working with

healthcare data.

Familiarity with:

EHR systems

Data privacy / compliance standards

Additional Notes

• Remote role, but

preference for candidates based in Southern California

• East Coast candidates will not be considered, NO exceptions,

due to collaboration constraints

Why This Role Matters

You’ll be contributing to a team that is

leveraging AI to improve patient care, accelerate clinical insights, and modernize healthcare systems at scale

. This is an opportunity to work on

meaningful, real-world AI applications

— not just theoretical models.