MLOps Engineer

Blue Signal Search; Santa Clara (CA); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Advanced GPU infra exposure

Collaborative engineering culture

Open source AI frameworks access

Competitive compensation

Career growth in AI infra

Job summary

Blue Signal Search is seeking an experienced MLOps Engineer to design, automate, and scale production AI infrastructure in Santa Clara, California. You will join a team building distributed GPU computing backbones for large-scale machine learning workloads and enterprise AI applications.

You will implement ML pipelines with Kubeflow and Airflow, orchestrate on Kubernetes and SLURM, and develop Python automation to improve reliability, performance, and operational efficiency across the platform.

Qualifications

• Five+ years in Infrastructure, DevOps, Platform Engineering, or ML Ops.
• Two+ years supporting production GPU/AI infrastructure.
• Production experience orchestrating distributed GPU workloads with Kubernetes, SLURM, Ray, or similar.
• Production ML pipelines with Kubeflow or Airflow.
• Deploying open source foundation models like Llama, Qwen, DeepSeek or similar.
• Strong Python programming and automation.
• Excellent cross-functional communication.
• Experience in AI-focused organizations or GPU cloud providers.

Responsibilities

• Design, maintain, and improve production infrastructure for distributed GPU ML workloads.
• Build and optimize automated ML workflows with Kubeflow, Airflow, or similar.
• Deploy and maintain open source LLMs in production ensuring performance and scalability.
• Orchestrate AI workloads across Kubernetes, SLURM, Ray, and similar platforms.
• Develop Python automation to streamline infra management and deployment processes.
• Monitor GPU resource utilization and improve scheduling efficiency.
• Collaborate with software engineers, AI researchers, and infra teams to productionize models.
• Create operational docs, automation tools, and deployment standards.
• Provide guidance on AI infra capabilities to internal and customer teams.

Skills

Python

MLOps

Kubernetes

Kubeflow

Airflow

SLURM

Ray

Automation

GPU infra

Tools

Kubeflow

Airflow

Kubernetes

Ray

SLURM

Job description

Our client is expanding a high performance AI infrastructure environment that supports large scale machine learning workloads for enterprise and emerging AI applications. They are seeking an experienced MLOps Engineer to help build, automate, and optimize the operational backbone behind distributed GPU computing environments. This is an opportunity to work alongside experienced infrastructure and AI engineering teams while solving complex challenges involving model deployment, orchestration, automation, and production reliability.

This Role Offers

• Opportunity to work on advanced GPU infrastructure supporting large scale AI initiatives.
• Highly collaborative engineering culture with significant technical ownership.
• Exposure to modern open source AI frameworks and distributed computing technologies.
• Competitive compensation package with comprehensive benefits.
• Long term career growth within an organization investing heavily in AI infrastructure.

What You Will Do

• Design, maintain, and improve production infrastructure supporting distributed GPU based machine learning workloads.
• Build and optimize automated ML workflows using orchestration platforms such as Kubeflow, Airflow, or similar technologies.
• Deploy, configure, and maintain open source large language models within production environments while ensuring performance, reliability, and scalability.
• Orchestrate AI workloads across Kubernetes, SLURM, Ray, and comparable distributed computing platforms.
• Develop Python based automation to streamline infrastructure management, deployment processes, and operational workflows.
• Monitor GPU resource utilization, improve scheduling efficiency, and help maximize overall infrastructure performance.
• Partner closely with software engineers, AI researchers, and infrastructure teams to transition experimental models into reliable production systems.
• Create operational documentation, automation tools, and deployment standards that improve platform reliability and supportability.
• Provide technical guidance to internal stakeholders and customer facing teams regarding AI infrastructure capabilities and deployment best practices.

Required Qualifications

• Five or more years of experience in Infrastructure Engineering, DevOps, Platform Engineering, or Machine Learning Operations.
• At least two years of hands on experience supporting production GPU or AI infrastructure environments.
• Demonstrated production experience orchestrating distributed GPU workloads using Kubernetes, SLURM, Ray, or similar technologies.
• Experience building and maintaining production ML pipelines using Kubeflow, Airflow, or equivalent orchestration platforms.
• Proven experience deploying and supporting open source foundation models such as Llama, Qwen, DeepSeek, or similar large language models.
• Strong Python programming and automation experience.
• Excellent communication skills with the ability to collaborate effectively across engineering organizations and enterprise customers.
• Previous experience working within an AI focused organization, GPU cloud provider, or comparable AI infrastructure environment.

Preferred Experience

• Familiarity with model serving optimization frameworks and inference acceleration technologies.
• Experience with infrastructure observability, monitoring, and performance analysis tools.Knowledge of distributed GPU networking and large scale compute environments.
• Experience supporting production AI platforms throughout the complete model lifecycle.