Machine Learning Engineer
Company: Odesus
Location: Los Angeles
Posted on: February 17, 2026
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Job Description:
Job Description Job Description Remote - Must work PST Summary:
Under the direction of Information Services Leadership, the
incumbent will be responsible for the full lifecycle management of
machine learning models, including design, build, and maintenance
of machine learning models. The MLOps Engineer will play an
integral role in implementing artificial intelligence solution. The
incumbent will partner with data scientists, data team members, and
clinical operations to deploy, monitor, and maintain machine
learning solutions that will improve patient care, support
operational excellence, and advance clinical research. The
incumbent will ensure seamless integration, automation, and scaling
of AI solutions within the existing infrastructure by leveraging
DevOps expertise. They will maintain and continuously improve MLOps
pipelines for monitoring, versioning, and deploying models in
production environments. The incumbent will be responsible for the
end-to-end lifecycle management of artificial intelligence
solutions and comes with DevOps experience, ensuring seamless
integration, deployment, and automation of systems. The MLOps
Engineer will implement best practices for testing, debugging, and
performance monitoring of AI systems to ensure reliability and
scalability. Minimum Education: Bachelor's degree in computer
science, artificial intelligence, informatics or closely related
field. Master's degree in computer science, engineering or closely
related field preferred. Minimum Experience: 3 or more years
relevant Machine Learning Engineer Experience. Proven experience
with: Artificial intelligence and machine learning platforms (e.g.,
AWS, Azure or GCP). 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). MLOps engineering principles, agile methodologies,
and DevOps life-cycle management. Technical writing and
documentation for AI/ML models and processes. Healthcare data and
machine learning use cases. Healthcare Expertise: Understanding of
healthcare regulations and standards, and familiarity with
Electronic Health Records (EHR) systems, including integrating
machine learning models with these systems. 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 Accountabilities: Production Deployment and
Model Engineering: Proven experience in deploying and maintaining
production-grade machine learning models, with real-time inference,
scalability, and reliability. Scalable ML Infrastructures:
Proficiency in developing end-to-end scalable ML infrastructures
using on-premise cloud platforms such as Amazon Web Services (AWS),
Google Cloud Platform (GCP), or Azure. Engineering Leadership:
Ability to lead engineering efforts in creating and implementing
methods and workflows for ML/GenAI model engineering, LLM
advancements, and optimizing deployment frameworks while aligning
with business strategic directions. AI Pipeline Development:
Experience in developing AI pipelines for various data processing
needs, including data ingestion, preprocessing, and search and
retrieval, ensuring solutions meet all technical and business
requirements. Collaboration: Demonstrated ability to collaborate
with data scientists, data engineers, analytics teams, and DevOps
teams to design and implement robust deployment pipelines for
continuous improvement of machine learning models. Continuous
Integration/Continuous Deployment (CI/CD) Pipelines: Expertise in
implementing and optimizing CI/CD pipelines for machine learning
models, automating testing and deployment processes. Monitoring and
Logging: Competence in setting up monitoring and logging solutions
to track model performance, system health, and anomalies, allowing
for timely intervention and proactive maintenance. Version Control:
Exp
Keywords: Odesus, Rancho Santa Margarita , Machine Learning Engineer, IT / Software / Systems , Los Angeles, California
