Operations Engineer

Salt; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

A leading utility company is seeking an MLOps/LLMOps Engineer to operationalize Large Language Model applications for enterprise-wide deployment. The role focuses on infrastructure, automation, and performance optimization to deliver GenAI solutions at scale, making it critical for the company's next-generation AI initiatives.

Qualifications

• Experience with Kubernetes and Docker required.
• Operating LLMs in production environments is essential.
• Experience in monitoring and optimizing GPU infrastructure.

Responsibilities

• Design scalable LLM deployment architectures.
• Build and maintain CI/CD pipelines for model retraining.
• Monitor model performance and optimize infrastructure.

Skills

Kubernetes

Docker

MLflow

Python

Bash

Infrastructure-as-Code

GPU infrastructure

cloud platforms

Job description

This range is provided by Salt. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Recruiter at SALT| Tech recruiter | Creative recruiter | Specialist in US market

A leading utility company is seeking an MLOps/LLMOps Engineer to help scale and operationalize Large Language Model (LLM) applications for enterprise-wide deployment. This role goes beyond traditional MLOps—focusing on the infrastructure, automation, and performance optimization needed to deliver GenAI solutions at scale.

• Design scalable LLM deployment architectures using Docker, Kubernetes, and cloud services
• Build and maintain CI/CD pipelines for model retraining, versioning, and updates
• Monitor model performance, inference latency, and data quality using custom observability stacks
• Optimize GPU infrastructure and compute resources to balance cost and speed
• Collaborate with security teams to ensure robust controls across all GenAI applications
• Hands-on experience with Kubernetes, Docker, and MLflow
• Proficiency in Python, Bash, and Infrastructure-as-Code (e.g., Terraform, Ansible)
• Strong knowledge of GPU infrastructure, cloud platforms (AWS, Azure, GCP), and performance tuning
• Experience operationalizing LLMs at scale in a production environment
• Duration: 12-month contract
• Location: 100% Remote
• Rate: $60–$73/hr (W2)

Apply now to help scale next-generation AI across one of the largest utility environments in the U.S.!

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Contract

Job function

• Industries

  Utilities

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