Responsibilities
- Support the development and execution of the infrastructure roadmap across DevOps, and MLOps, aligned with product, and data/AI growth plans.
- Partner with engineering, data, and ML teams to ensure scalability, reliability, security, and automation are built into both application infrastructure and machine learning workflows.
- Help evaluate and adopt DevOps and MLOps tools that improve system efficiency, observability, developer experience, model deployment, and operational reliability.
- Contribute to platform standards that make infrastructure, CI/CD, data pipelines, and ML systems more repeatable, secure, and easier to operate.
- Support the evolution of cloud-native practices that enable faster product delivery while also preparing the platform for future AI and ML initiatives.
- Build and manage infrastructure to deploy ML models into production reliably using CI/CD pipelines, Flask-based APIs, and orchestration tools (e.g., Airflow, Kubeflow, or Argo Workflows).
- Automate training pipelines, model registry, validation, deployment, and rollback strategies using tools such as Amazon SageMaker Interface and Postman for testing.
- Build systems to monitor model performance, latency, data drift, and resource usage using Amazon CloudWatch, Prometheus, and Grafana.
- Design and maintain tools and systems to support model versioning, experiment tracking (e.g., MLflow, Amazon SageMaker Studio Notebooks), and reproducible training workflows.
- Operate across GCP and AWS to manage training/inference infrastructure, BigQuery datasets, and GPU workloads.
- Use tools like Terraform or CloudFormation to manage cloud infrastructure in a scalable, repeatable manner.
- Work with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support their end-to-end ML workflows.
- Partner with Product and Data teams to streamline CI/CD workflows, GitOps practices, and deployment processes that support both application delivery and data/ML workflows.
- Work closely with Data teams to support reliable infrastructure for data pipelines, model experimentation, training workflows, and production ML deployments.
- Collaborate with Product teams to understand platform needs and ensure infrastructure decisions support product reliability, scalability, and delivery speed.
- Share documentation, runbooks, and best practices that help Product and Data teams deploy, monitor, and troubleshoot systems with more autonomy.
- Support a collaborative DevOps and MLOps culture by helping teams adopt automation, observability, and repeatable deployment patterns.
- Stay current with cloud-native, DevOps, data infrastructure, and MLOps trends, identifying tools, patterns, and practices that can improve reliability, automation, scalability, and delivery speed.
- Continuously evaluate infrastructure, CI/CD pipelines, data workflows, model deployment processes, performance, cost, and efficiency, recommending improvements that align with product, engineering, and data team goals.
- Contribute to documentation, runbooks, incident reviews, and post-mortem processes to strengthen operational learning across both DevOps and MLOps practices.
- Help define and share best practices for infrastructure-as-code, GitOps, observability, secure deployments, data pipeline reliability, and ML workflow automation.
- Look for opportunities to simplify systems, reduce manual work, and improve the developer and data team experience through better tooling, automation, and platform standards.
Requirements
- 2–5 years of hands-on DevOps or cloud engineering experience in production environments.
- Expertise with Kubernetes (EKS), Helm, and microservices.
- 1-2 years AWS and SageMaker Experience.
- 1-2 years creating data pipelines
- 1-2 years with LLM deployments on Kubernetes
- 1-2 years with kubernetes clusters using nodes that have GPU
- Proven track record using Terraform for scalable, auditable Infrastructure as Code.
- A collaborative, solution-oriented mindset and a passion for automation and continuous improvement.
Work Arrangement
Hybrid
Additional Information
- Our approach to compensation: We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope.
- We evaluate compensation bi-annually, including performance and market-related factors.
- Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting.
- Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.
- To comply with local legislation and ensure transparency, we share salary ranges on all job postings.
- Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted.
- Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.
