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Mlops Jobs in California (NOW HIRING)

Redolent, Inc.

Prinicipal MlOps Engineer

Sunnyvale, CA · On-site

Principal MLOps Engineer Location: Sunnyvale, CA Job Type: - Contract - 12+ Months Department: Data Science / Machine Learning About the Role We are seeking an experienced Principal MLOps Engineer to ...

Cyber 1 Armor

Senior MLOps / LLMOps Engineer

Milpitas, CA · On-site

$119K - $163K/yr

Senior MLOps / LLMOps Engineer Location : Milpitas 4 days onsite contracts We are looking for a Senior MLOps / LLMOps Engineer to help standardize and enhance enterprise ML and GenAI deployment ...

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data Engineering and Analytics team. You will build and operate the infrastructure that takes AI and machine ...

Marlabs

Computational Biology MLOps Engineer

San Diego, CA · On-site

$118K - $139K/yr

Computational Biology MLOps Engineer | About You As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico ...

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data Engineering and Analytics team. You will build and operate the infrastructure that takes AI and machine ...

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data Engineering and Analytics team. You will build and operate the infrastructure that takes AI and machine ...

Quince

Sr. Engineering Manager, MLOps

Palo Alto, CA

THE ROLE Senior Engineering Manager, MLOps We are seeking a Senior Engineering Manager, MLOps to join our growing team. The ideal candidate is a technical visionary with a proven track record of ...

Nvidia Corporation

Senior MLOps Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

NVIDIA is seeking a Senior MLOps Engineer to join our Autonomous Driving organization in Santa Clara, CA. This individual contributor will design and operate end-to-end data and ML pipelines for ...

Nvidia

Senior MLOps Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

NVIDIA is seeking a Senior MLOps Engineer to join our Autonomous Driving organization in Santa Clara, CA. This individual contributor will design and operate endtoend data and ML pipelines for NVIDIA ...

Nvidia

Senior MLOps Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

NVIDIA is seeking a Senior MLOps Engineer to join our Autonomous Driving organization in Santa Clara, CA. This individual contributor will design and operate endtoend data and ML pipelines for NVIDIA ...

smart folks inc

MLOps Developer

Sunnyvale, CA · On-site

MLOPS Ray Developer Location: Austin, TX/ Sunnyvale, CA/ Remote Duration: Long-term * Deep understanding of Ray, Operate, monitor, and triage all aspects of our production and non-production ...

Redolent, Inc.

MLOps Engineer

Sunnyvale, CA · On-site

MLOps Engineer Duration: 12+ Months Location:Sunnyvale CA - hybrid Rate: DOE Key Responsibilities : Design and implement scalable model serving platforms for both batch and real-time inference Build ...

4i Americas

MLOPs Engineer

Concord, CA · On-site

Ensure best practices in scalability, security, and reliability Required Skills & Qualifications * 2-5 years of experience in MLOps, DevOps, or ML Engineering * Strong proficiency in Python * Hands ...

GyanSys Inc.

CA · On-site

$121K - $167K/yr

GyanSys is looking for Senior LLMOps / MLOps Engineer to join one of our direct clients in Santa Clara, CA Please see the details below and let me know if you are interested, * 5-7 years of ...

Nace AI

Senior MLOps Engineer

Palo Alto, CA · On-site

$122K - $168K/yr

As a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI's models from research to reliable, production-grade systems. Our infrastructure generates task-specific Small Language ...

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Mlops information

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in California?

The most popular types of Mlops jobs in California are:

What are popular job titles related to Mlops jobs in California?

For Mlops jobs in California, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in California look for?

The top searched job categories for Mlops jobs in California are:

What cities in California are hiring for Mlops jobs?

Cities in California with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in California as of August 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

Prinicipal MlOps Engineer

Sunnyvale, CA • On-site

Redolent, Inc.
IT Services • 51 - 200 employees
Learn more about Redolent, Inc.external

Contractor

Re-posted 12 hours ago

Job description

Job Title: Principal MLOps Engineer
Location: Sunnyvale, CA
Job Type: - Contract - 12+ Months
Department: Data Science / Machine Learning
About the Role
We are seeking an experienced Principal MLOps Engineer to lead and scale our machine
learning operations, ensuring eJicient, secure, and reliable ML model deployments. As a
senior technical leader, you will be responsible for designing and implementing a cutting-
edge MLOps framework, driving automation, and enhancing ML infrastructure to support
large-scale, mission-critical applications. This role requires deep expertise in MLOps best
practices, cloud architecture, and DevOps principles, along with strong leadership and
collaboration skills to guide engineering teams and stakeholders.
Key Responsibilities
  • Architect and lead the development of scalable and robust ML infrastructure to support the entire model lifecycle, from experimentation to production.
  • Establish MLOps best practices, ensuring automation, reproducibility, versioning, and monitoring of models in production.
  • Design and implement CI/CD pipelines for machine learning models, integrating security, compliance, and performance optimization.
  • Drive ML observability strategies, implementing monitoring tools for detecting model drift, data drift, and performance degradation.
  • Optimize and manage cloud-based ML workloads using AWS, GCP, or Azure, ensuring cost-eJiciency and scalability.
  • Lead and mentor a team of MLOps engineers, collaborating closely with data scientists, software engineers, and DevOps teams.
  • Define infrastructure as code (IaC) using Terraform, Kubernetes, and containerization tools to standardize deployments.
  • Enhance ML model serving architectures, leveraging Kubernetes, serverless computing, or specialized model-serving frameworks.
  • Implement robust security frameworks for ML workflows, ensuring data privacy, access control, and compliance with industry regulations.
  • Stay ahead of industry trends, evaluating and integrating new technologies to improve automation and eJiciency in ML workflows.

Requirements
Technical Skills
  • Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
  • Deep knowledge of CI/CD pipelines, DevOps practices, and cloud-native architectures.
  • Strong experience with Kubernetes, Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.).
  • Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.
  • Proficiency in monitoring and observability tools like Prometheus, Grafana, ELK Stack, or Datadog for ML workloads.
  • Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
  • Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
  • Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.

Soft Skills
  • Proven leadership in architecting, deploying, and managing large-scale M infrastructure.
  • Strong ability to mentor and lead teams, fostering best practices and knowledge sharing.
  • Excellent problem-solving and critical thinking skills to tackle complex ML engineering challenges.
  • EJective communication and collaboration with cross-functional teams, including engineering, product, and business stakeholders.

Education & Experience
  • Bachelor's or master's degree in computer science, Machine Learning, Data Engineering, or a related field.
  • 7+ years of experience in MLOps, DevOps, or ML infrastructure engineering.
  • Proven track record of leading ML deployment initiatives at scale in enterprise or high growth environments.
Redolent logo

About Redolent

Sourced by ZipRecruiter

Redolent, a dynamic and rapidly expanding company committed to excellence in software solutions, where success is fueled by a combination of technical expertise and efficient management practices. Our solutions create a measurable delta in our clients’ productivity and profitability, contributing to their growth and success.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

San Jose, CA, US

Year founded

2008

Social media

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