Senior Devops Engineer- ML Engineering Support

About the Role

We are seeking a talented and experienced Senior Software Engineer, DevOps/SRE to join our dynamic team and play a critical role in supporting Machine Learning Engineering activities. The ideal candidate will have a strong background in DevOps practices, cloud infrastructure management, automation, and MLOps tooling, along with team leadership skills.

If you have a proven track record architecting and scaling ML/AI platforms, enjoy solving intriguing system challenges at internet-scale, are innovative at heart, and thrive in building infrastructure that accelerates ML experimentation and deployment — this role might be a great fit for you!

What You’ll Be Doing

• Provide technical leadership and guidance to DevOps/SRE engineers supporting ML Engineering initiatives; mentor team members in best practices, technologies, and methodologies.
• Design, implement, and maintain scalable and resilient cloud infrastructure (AWS & GCP) optimized for ML workloads, including GPU/TPU orchestration and distributed training.
• Partner with ML Engineers to streamline the end-to-end ML lifecycle: data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Build and maintain CI/CD pipelines for ML applications and models using GitHub Actions, GitLab CI/CD, Argo, or Tekton.
• Integrate with MLOps platforms (e.g., MLflow, Kubeflow, Airflow, SageMaker, Vertex AI) to ensure reproducibility and traceability of experiments.
• Lead incident response efforts for ML-serving and training infrastructure, minimizing downtime and ensuring high availability.
• Implement observability practices for ML workloads, including model performance monitoring, drift detection, and metrics via Prometheus, Grafana, and Datadog.
• Collaborate with security and compliance teams to ensure adherence to data governance, PCI, SOX, and AI/ML data security standards.
• Optimize system resources for large-scale ML jobs, including auto-scaling GPU clusters, cost optimization, and quota management.
• Drive continuous improvement across DevOps + MLOps processes; proactively identify areas for enhancement.
• Maintain clear documentation and foster a culture of knowledge sharing across DevOps, ML, and Data Engineering teams.
• Participate in 24x7 on-call rotation, with availability to work with global teams in the event of critical outages.

We’re Excited if You Have

• 8+ years of experience in DevOps/SRE roles, including at least 2–3 years supporting ML or data-intensive workloads.
• Strong programming skills in Python or Go; experience building internal tools and automation for ML pipelines.
• Hands-on experience with Kubernetes, Docker, ECS/EKS/GKE, and service mesh tools such as Istio or Envoy.
• Familiarity with GPU/accelerator orchestration (NVIDIA GPU Operator, KubeFlow, Slurm, Ray, or similar).
• Experience with Infrastructure as Code (IaC): Terraform, Helm, Ansible, or CloudFormation.
• Deep understanding of distributed systems, microservices architecture, and cloud-native design patterns.
• Exposure to MLOps tools: MLflow, Kubeflow Pipelines, Airflow, Argo, Vertex AI, or SageMaker.
• Strong proficiency in cloud platforms (AWS and GCP required; Azure a plus).
• Knowledge of data engineering concepts (object storage like S3/GCS, parquet/ORC, data versioning with DVC or Delta Lake).
• Experience with networking, security, and compliance (role-based access, VPC design, encryption, auditing).
• Demonstrated success in cross-functional collaboration with ML, Data, and Product teams.
• Preferred certifications: Certified Kubernetes Administrator (CKA), AWS Certified DevOps Engineer, Google Professional Cloud DevOps Engineer, NVIDIA Deep Learning Institute courses.
• AI literacy and curiosity, You have either tried Gen AI in your previous work or outside of work or are curious about Gen AI and have explored it. 
• BS Degree in Computer Science or equivalent experience.