MLOps Engineer
Core
Design, deploy, and maintain high-scale infrastructure for model serving, LLM gateways, observability, and research workloads across multi-cloud environments.
Role type
Senior MLOps Engineer (Infrastructure)
Builds
Production ML/LLM systems, GPU clusters, and secure connectivity for model serving and inference.
Domain
AI Security, Cloud Infrastructure, Machine Learning Operations
Deliverable
infrastructure
Required skills
Kubernetes, Docker, Terraform, CI/CD pipelines, Linux, scripting, networking fundamentals, cloud platforms (AWS/GCP/Azure), monitoring and logging
Preferred skills
GPU workload management, LLM serving and inference-aware load balancing, experiment tracking tooling, vector databases, distributed compute frameworks, government cloud deployments
Technologies
Kubernetes, Docker, Terraform, AWS, GCP, Azure, Linux
Responsibilities
Design and maintain infrastructure for model serving and LLM gateways; own GPU infrastructure capacity and cost efficiency; build secure private connectivity between clouds; monitor systems and resolve issues; automate infrastructure provisioning and tooling; support research GPU clusters and ML workloads.
Seniority
Senior, hands-on IC
