Senior Platform Engineer – ML Infrastructure
We're hiring a Senior Platform Engineer – ML Infrastructure in the AI/ML infrastructure and deep-tech industry .
We're seeking an experienced engineer to join our core infrastructure team. This role will be critical in designing and scaling the foundational systems that power AI products. If you're passionate about building robust, efficient, and innovative ML infrastructure, we'd love to hear from you.
Job Requirements
Experience in DevOps engineering with solid experience in Docker, Kubernetes and shell scripting.
Experience working with distributed GPU systems and ML infrastructure.
Experience building robust infrastructure for training and serving machine learning models at scale.
Experience with tools for Data Annotation, Data Curation, Model Registry, Model Serving and Workflow Orchestration.
Experience building LLM powered systems like Retrieval Augmented Generation (RAG) or Agentic Workflows at scale.
You can work your way out of an unfortunate 'CUDA_ERROR_VERSION_MISMATCH' error or better prevent them altogether.
Ability to setup up multi node kubernetes clusters on on-premise Datacenter and/or deep knowledge on managed kubernetes services, experience with kube native tooling and ecosystem.
Expertise with configuration management systems like Ansible and IaaC (Terraform/pulumi).
Experience with setting up and maintaining scalable data lakes and data processing pipelines.
Ability to set up highly available services and databases.
Expertise across cloud platforms - AWS, Azure and GCP and their services.
Understanding of networking principles, load balancing, DNS configurations, proxies etc.
Experience building robust integration and deployment pipelines.
Strong problem solving, debugging and analytical skills. Ability to plan, execute projects, to deliver in time and with quality.
Experience with ML orchestration services like kubeflow, flyte, prefect etc.
Experience with distributed computing frameworks like ray
Knowledge of Strong understanding of Role-Based Access Control (RBAC) principles to effectively manage permissions and access control.
Knowledge on MLOps concepts like model and data versioning, orchestration and model serving.
High level understanding of modern distributed applications and their design.
Job Responsibilities
Take ownership of infrastructure layer for our on-premise and cloud deployments
Assist development teams in designing scalable and portable applications.
Establish best practices within the organisation and help developers ship fast without breaking things.
Product ownership and managing periodic reporting on the progress to the management and senior leadership.
A team player who values collaboration, innovation, and inclusion.
Mentor team members to adopt a platform-first mindset within the organisation.
We are looking for the right teammate with a shared vision and a passion to build really impactful technology. If you feel that you fit most of our requirements but not all, please don't hesitate to apply!
Skills: ml orchestration services,workflow orchestration,data processing pipelines,cuda troubleshooting,networking,distributed gpu systems,kubernetes,computing,docker,model registry,ml,infrastructure as code (terraform/pulumi),managed kubernetes services,shell scripting,mlops,devops,machine learning infrastructure,data,load balancing,llm systems,data lakes,model serving,ansible,data annotation,high availability services,kubernetes clusters,gcp,role-based access control (rbac),data curation,azure,integration and deployment pipelines,distributed computing frameworks,aws,dns configurations

Senior Platform Engineer (ML infrastructure)