ML Ops Engineer

Not available for c2c engagements | Vendors marketing candidates will be blocked

Must be eligible for w2 employment without sponsorship

Must be local to the LA/Burbank Area

Must have experience:

• Python (10 years)
• Terraform
• Deploying ML models on AWS Sage Maker
• CI/CD Automation

About the Role

We're building a brand-new application from the ground up and seeking an experienced MLOps Engineer to architect and operationalize our data science infrastructure. This is a greenfield opportunity to establish best practices, build scalable deployment pipelines, and bridge the gap between data science innovation and production-ready systems.

You'll work hands-on with our team of Data Scientists, an ML Ops Engineer, Application Architect, and Infrastructure Architect to create seamless CI/CD pipelines that deploy streaming ML models at scale.

What You'll Do

• Build & maintain cloud infrastructure for data science and machine learning workflows using infrastructure-as-code principles
• Design and implement CI/CD pipelines that operationalize data science models from development to production
• Deploy streaming ML models on AWS Sage Maker and manage the full lifecycle of model deployment
• Implement containerization strategies with Docker and Kubernetes for scalable model serving
• Set up monitoring and observability using Splunk and Data Dog to ensure system reliability and performance
• Automate configuration management using Ansible for seamless deployments across environments
• Collaborate closely with data scientists to understand model requirements and translate them into robust production systems

What You Bring

Required Experience

• 10+ years of Python programming experience with a focus on automation and infrastructure
• 5+ years of hands-on experience with Kubernetes, Terraform, and cloud infrastructure
• Proven track record deploying streaming ML models on AWS Sage Maker
• Deep expertise in CI/CD automation and establishing deployment pipelines from scratch
• Strong experience with containerization (Docker) and orchestration (Kubernetes)
• Infrastructure-as-Code proficiency with Terraform
• Configuration management experience with Ansible or similar tools
• Git and scripting for version control and automation workflows

Preferred Skills

• Experience with MLOps practices and ML model lifecycle management
• Familiarity with Managed Streaming for Apache Kafka (MSK)
• Knowledge of Splunk and Data Dog for monitoring and observability
• Background in data engineering or data science domains
• AWS certifications or equivalent cloud expertise

What Makes This Role Unique

• Greenfield project:
  Shape the architecture and practices from day one
• No on-call rotation:
  Focus on building quality systems without overnight interruptions
• Collaborative environment:
  Work directly with data scientists and architects to solve complex problems
• Impact-driven:
  Your infrastructure will directly enable groundbreaking data science work

What We're Looking For

Beyond technical skills, we value:

• Excellent communication skills to collaborate across technical and non-technical stakeholders
• Systems thinking to design for scalability, reliability, and maintainability
• Problem-solving mindset to navigate ambiguity in a new application build
• Passion for automation and eliminating manual processes

Team Structure

You'll join as an individual contributor working within a cross-functional team that includes Data Scientists, an ML Ops Engineer, Application Architect, and Infrastructure Architect. This role offers significant autonomy and ownership over the Dev Ops and infrastructure domain.