MLOps Engineer // REMOTE / Work-Life Balance

About the role

An industry-leading technology and analytics organization is hiring an AI/ML Operations Engineer to support its rapidly growing artificial intelligence initiatives. This full-time opportunity offers the chance to work with modern cloud technologies, machine learning platforms, containerized environments, and production AI systems. The ideal candidate will have experience deploying machine learning solutions, building cloud infrastructure, and supporting MLOps best practices across enterprise environments. This is an excellent opportunity for someone who wants to be at the center of real-world AI deployment without being confined to research-focused responsibilities. The organization is investing heavily in AI innovation and is seeking an engineer who can help bridge development and operations. Team members are encouraged to grow their expertise, influence best practices, and help scale cutting-edge AI capabilities used across diverse client engagements.

Responsibilities

• Deploy machine learning models into production environments
• Build and maintain cloud infrastructure for AI workloads
• Support MLOps best practices across enterprise environments
• Manage Kubernetes clusters and containerized environments
• Develop and maintain CI/CD pipelines for AI systems
• Monitor, observe, and respond to incidents in production AI systems

Required Skills & Experience

• 3+ years of experience in MLOps, Platform Engineering, or Infrastructure Engineering
• Experience deploying machine learning models into production environments
• Strong Python development and scripting skills
• Hands-on experience with AWS cloud services
• Experience with Databricks or comparable data/AI platforms
• Experience building and maintaining CI/CD pipelines
• Docker and Kubernetes experience
• Monitoring, observability, and incident response experience
• Bachelor's Degree in Computer Science, Engineering, or related field (or equivalent experience)

Desired Skills & Experience

• Exposure to large language models (LLMs) and generative AI systems
• Experience with AI cost optimization and resource management
• SQL and data analytics experience
• Infrastructure-as-Code experience
• Model versioning and reproducibility knowledge
• Experience supporting enterprise-scale AI workloads

Tech Breakdown

• 40% Cloud Infrastructure (AWS, Databricks)
• 25% Kubernetes & Container Management
• 20% CI/CD & Deployment Automation
• 15% Monitoring, Observability & Reliability

Daily Responsibilities

• 80% Hands On Engineering
• 5% Strategic Planning
• 15% Team Collaboration

Pay

Competitive hourly rate

Benefits

• Medical, Dental, and Vision Insurance
• Career Development Opportunities

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.