Senior Machine Learning Engineer
Core
Design, build, and operate MLOps infrastructure to deploy machine learning and large language models safely and at scale.
Role type
Senior MLOps Engineer
Builds
End-to-end MLOps platform, CI/CD pipelines, and scalable model-serving endpoints for AWS and Databricks
Domain
Cloud Infrastructure, MLOps, Large Language Models (LLMs)
Deliverable
production ML models
Required skills
MLOps, AWS cloud architecture, Databricks, CI/CD pipeline design, Kubernetes, containerization, observability, security governance, Infrastructure as Code
Preferred skills
LLM deployment patterns, vector search, agentic workflows, disaster recovery planning
Technologies
AWS, Databricks, MLflow, Terraform, Kubernetes, Docker, GitHub Actions, Jenkins, API Gateway, ECS, EKS, Lambda
Responsibilities
Design scalable MLOps platform architectures; Build automated CI/CD pipelines for model artifacts and infrastructure; Operate secure, low-latency inference endpoints for traditional ML and LLMs; Implement reliability mechanisms including failover, rollback, and disaster recovery; Establish end-to-end observability for model performance and data drift; Enforce security controls and governance for model-serving environments; Automate infrastructure provisioning using Infrastructure as Code
Seniority
Senior, hands-on IC
