Senior MLOps Engineer

What you'll do

• Design, build, and scale production ML pipelines for model deployment, serving, monitoring, and lifecycle management.
• Build and improve CI/CD workflows for ML models, including automated testing, versioning, and release processes.
• Develop and operate scalable infrastructure to support real-time and batch model inference.
• Implement monitoring and observability for ML systems to track performance, health, and reliability.
• Expand MLOps capabilities into GenAI and AgentOps, including evaluation, tracing, and cost management.
• Maintain and improve infrastructure using Infrastructure as Code tools like Terraform.
• Support ML lifecycle capabilities such as experiment tracking, model registries, and workflow orchestration.
• Mentor other engineers and establish engineering standards for reproducibility, security, and governance.

What we're looking for

• Bachelor’s degree in Computer Science, Information Technology, Information Systems, a related field, or equivalent relevant work experience.
• 5+ years of experience in software engineering, DevOps, ML engineering, MLOps, or a related discipline supporting production machine learning systems.
• Strong Python programming skills and software engineering fundamentals.
• Hands-on production MLOps experience across CI/CD, model deployment, model serving, and monitoring.
• Hands-on experience with AWS cloud-based infrastructure, Docker, and Kubernetes.
• Experience with Infrastructure as Code, such as Terraform.
• Hands-on experience contributing to at least one LLM or agentic AI project, including RAG, evaluation, or guardrails.
• Experience with Databricks and MLflow (preferred); experience with GitHub Actions and AWS CloudWatch (preferred).