MLOps/Platform Engineer

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‍POSITION

We are seeking an MLOps / Platform Engineer to design, build, and scale our GPU-enabled AI/ML infrastructure. This role will empower our AI researchers, data engineers, and product teams by providing them with robust, Kubernetes-based platforms for containerized workloads.

The ideal candidate has a platform engineering mindset—focused on enabling teams through self-service tooling, automation, and reliability. You will take ownership of our GPU infrastructure, ensuring workloads scale efficiently, pipelines run smoothly, and deployments are reliable across environme

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‍WHAT YOU’LL BE DOING?
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·   Design, build, and operate Kubernetes-based GPU infrastructure for AI/ML research and production workloads.

·   Provide self-service deployment capabilities to AI researchers, data engineers, and product teams.

·   Implement and manage continuous delivery pipelines using Kubernetes-native deployment tools (e.g., ArgoCD).

·   Configure and optimize GPU workloads (CUDA, drivers, device plugins, and scheduling strategies).

·   Implement autoscaling strategies for both CPU and GPU workloads on Kubernetes clusters.

·   Partner with data teams to ensure platforms are tailored to data pipelines, training workflows, and ML model lifecycle needs.

·   Contribute to the adoption of ML orchestration and experiment tracking platforms (e.g., Kubeflow, MLflow, ClearML, Anyscale).

·   Support notebook environments for experimentation (e.g., JupyterHub).

·   Act as a platform engineering lead for AI/ML workloads—adopting the best practices, reliability, and cost-efficiency.

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WHAT YOU’LL BRING?
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·    Strong hands-on experience with Kubernetes (designing, operating, and troubleshooting clusters).

·    Proven experience deploying workloads with ArgoCD or similar GitOps tools.

·    Exposure to ML platforms such as Kubeflow, MLflow, ClearML, or Anyscale.

·    Prior experience working with data engineering teams or ML model lifecycle management.

·    Experience enabling notebook-based workflows (JupyterHub, SageMaker Studio, etc.).

·    Knowledge of cloud platforms (AWS, GCP, or Azure) and their GPU offerings.

·    Strong collaboration and communication skills—able to partner effectively with researchers, engineers, and product teams.
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NICE TO HAVES?
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·   Hands-on experience with GPU configuration, CUDA, or NVIDIA ecosystem tools (NCCL, Triton Inference Server).

·   Knowledge of autoscaling patterns for AI/ML workloads on Kubernetes (KEDA, HPA, VPA, custom controllers).

·   Experience with observability and monitoring tools (Prometheus, Grafana, Loki, ELK, etc.).

·   Solid understanding of containerization (Docker, OCI) and CI/CD pipelines.

·   Familiarity with infrastructure as code (Terraform, Helm, Kustomize).

·   Background in platform engineering, SRE, or DevOps with a focus on enabling other teams.

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THE RECRUITING PROCESS

We are committed to keeping our recruitment process short and transparent. Here’s how it looks like:

1. Application: Send us your CV or LinkedIn profile. You can also just write a few words about yourself.

2. Case Study: We might send you a task to solve.

3. Talent & Culture Interview: Let’s talk about your experience & expectations and see what we can achieve together.

4. Technical Interview: You will meet with future team lead to go over the case and your technical capabilities in detail.

5.  Reference Check: We will contact your references to gain insights into your past experiences and work ethic.

6. Welcome Aboard! You are now a part of the team.

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