Senior ML Platform Engineer
Posted 28 days ago · 0 applicants
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The role in plain words
This role focuses on building and maintaining the infrastructure required to power secure, production-grade AI in an air-gapped environment. You will develop and optimize ML pipelines, model serving, experiment tracking, vector databases, and GPU orchestration. Day-to-day work involves collaborating with AI developers, data scientists, and software engineers to deliver scalable and reliable AI solutions.
- 5+ years of experience in MLOps, ML Platform, DevOps, Infrastructure, or Software Engineering with hands-on experience supporting production ML/AI platforms
- Kubernetes
- Docker/containers
- Linux
- ML lifecycle tools (e.g., MLflow, Kubeflow, Airflow)
- Experience with RAG infrastructure and vector databases (e.g., pgvector, Milvus, Weaviate, Qdrant)
- Experience with CI/CD, Infrastructure as Code (Terraform, Helm, Ansible), and AI gateways or LLM serving platforms
Extracted from the job description · kept up to date automatically
Who this suits
This role suits experienced engineers with over five years of experience in MLOps, DevOps, or infrastructure who have strong Kubernetes and Python scripting skills. It is less ideal for those without hands-on experience supporting production-level machine learning platforms.
Full job description
Original listing · kept for referenceabra is seeking for a Senior ML Platform Engineer . We're looking for a hands-on Senior ML Platform Engineer (MLOps) to build and maintain the infrastructure that powers secure, production-grade AI in an air-gapped environment. In this role, you'll develop and optimize ML pipelines, model serving, experiment tracking, model registries, vector databases (RAG), dataset versioning, and GPU orchestration. You'll work closely with AI developers, data scientists, and software engineers to deliver scalable, secure, and reliable AI solution
Requirements: Must have: • 5+ years of experience in MLOps, ML Platform, DevOps, Infrastructure, or Software Engineering with hands-on experience supporting production ML/AI platforms. • Strong experience with Kubernetes (or OpenShift/Rancher), Docker/containers, Linux, and production environments. • Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, Airflow) and model serving frameworks (e.g., vLLM, Triton, KServe). • Proficiency in scripting (Python/Bash) and experience working closely with ML Engineers and Software Developers. Nice to have: • Experience with RAG infrastructure and vector databases (e.g., pgvector, Milvus, Weaviate, Qdrant). • Experience with CI/CD, Infrastructure as Code (Terraform, Helm, Ansible), and AI gateways or LLM serving platforms.
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- 5+ years of experience in MLOps, ML Platform, DevOps, Infrastructure, or Software Engineering with hands-on experience supporting production ML/AI platforms, Kubernetes, Docker/containers, Linux, ML lifecycle tools (e.g., MLflow, Kubeflow, Airflow)