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) • Kubernetes • Terraform • Deploying ML models on AWS SageMaker • 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 SageMaker and manage the full lifecycle of model deployment • Establish infrastructure-as-code standards using Terraform to ensure reproducible, version-controlled environments • Implement containerization strategies with Docker and Kubernetes for scalable model serving • Set up monitoring and observability using Splunk and DataDog 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 SageMaker • 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 DataDog 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 DevOps and infrastructure domain.