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Llmops Jobs in California (NOW HIRING)

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 ...

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 ...

As a Staff MLOps Engineer with a focus in LLMOps , you'll be at the core of building and scaling the technical infrastructure for AI/ML systems. You will: * Build reusable CI/CD workflows for model ...

As a Senior MLOps Engineer with a focus in LLMOps , you'll be at the core of building and scaling the technical infrastructure for AI/ML systems. You will: * Build reusable CI/CD workflows for model ...

The application window is expected to close on: 10/30/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received . Meet the Team The team ...

New

Knowledge of MLOps / LLMOps practices. * Experience with AI governance, security, monitoring, and observability. * Experience using AI-assisted development tools such as Claude Code and Codex.

Senior, Software Engineer

Cupertino, CA · On-site

$117K - $234K/yr

You will design, build, and operate the tools that help in developing, scaling, and monitoring cutting-edge technology -- including GenAI and LLMOps pipelines. You must be able to triage complex ...

Senior, Software Engineer

Milpitas, CA · On-site

$117K - $234K/yr

You will design, build, and operate the tools that help in developing, scaling, and monitoring cutting-edge technology -- including GenAI and LLMOps pipelines. You must be able to triage complex ...

Senior, Software Engineer

San Jose, CA · On-site

$117K - $234K/yr

You will design, build, and operate the tools that help in developing, scaling, and monitoring cutting-edge technology -- including GenAI and LLMOps pipelines. You must be able to triage complex ...

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Llmops information

What is the difference between Llmops vs Data Scientist?

AspectLlmopsData Scientist
Required credentialsKnowledge of machine learning, AI frameworks, cloud platformsStatistics, programming, data analysis skills
Work environmentAI/ML teams, cloud environments, deployment pipelinesData analysis, modeling, reporting in various industries
Employer usageTech companies, AI startups, research labsFinance, healthcare, tech, retail

While both roles involve working with data and machine learning, Llmops focuses on deploying and maintaining large language models in production environments, requiring expertise in AI infrastructure. Data Scientists primarily analyze data, build models, and generate insights. Llmops professionals ensure models operate efficiently at scale, whereas Data Scientists develop the models and interpret results.

What cities in California are hiring for Llmops jobs?

Cities in California with the most Llmops job openings:

Infographic showing various Llmops job openings in California as of August 2026, with employment types broken down into 81% Full Time, 2% Temporary, and 17% Contract. Highlights an 82% In-person, 3% Hybrid, and 15% Remote job distribution.

Senior MLOps / LLMOps Engineer

Cyber 1 Armor

Milpitas, CA • On-site

$119K - $163K/yr

Other

Re-posted 6 days ago


Job description

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 pipelines.
Key Skills:
Strong hands-on experience with Databricks and MLflow
Experience building and maintaining MLOps/LLMOps platforms
Cloud expertise in Azure and/or Google Cloud Platform
CI/CD pipeline development and automation
Model deployment, monitoring, and lifecycle management
Kubernetes, Docker, Infrastructure as Code (Terraform preferred)
Experience supporting GenAI/LLM applications in production
Knowledge of model evaluation, observability, governance, and release management
Responsibilities:
Standardize MLOps and LLMOps workflows across teams
Build and optimize CI/CD pipelines for ML and GenAI applications
Deploy, monitor, and manage models in production environments
Establish best practices for MLflow, model governance, and operational excellence
Collaborate with data science, platform, and engineering teams