Senior/ Lead Engineer - MLOps/ DataOps/DevOps - AI Engineering

About the Role

Key Responsibilities

• Design, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as GitHub Actions, Terraform, Helm, ArgoCD, Crossplane, and others.
• Automate infrastructure provisioning and configuration management using cloud-native services (preferably AWS) with tools like Terraform, CloudFormation, or Crossplane.
• Design, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS.
• Collaborate with development teams to optimize performance, deployment, and cost.
• Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of data and ML infrastructure.
• Work closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deployment.
• Implement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similar.
• Develop and maintain automated pipelines for model retraining, monitoring drift, and versioning in production.
• Support experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systems.
• Ensure cloud infrastructure is secure, compliant, and cost-efficient, following best practices in governance, identity, and access management.

Requirements

• 8+ years experience in DataOps, MLOps, or related fields, with at least 2+ years focused on ML model operationalization and workflow automation.
• Proficient in AWS services including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECS.
• Strong experience with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and Helm.
• Experience with CI/CD for ML pipelines, GitOps practices, and tools like GitHub Actions, Jenkins, or Argo Workflows.
• Strong scripting and automation skills using Bash, Python, or GitHub workflows.
• Understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetry).
• Experience with feature stores (e.g., Feast, Tecton) and knowledge of data mesh, lakehouse architectures, or modern data stack concepts.
• Comfortable working with structured and unstructured data across various storage and pipeline systems.
• Solid understanding of security best practices for cloud and Kubernetes environments, including secrets management, identity & access control, and policy enforcement.
• Excellent collaboration and communication skills, with a proven ability to work effectively in cross-functional, globally distributed teams.
• Bachelor/Master's degree in computer sciences, Engineering, or a related discipline, or equivalent hands-on industry experience.

Nice to Have

• Knowledge of data governance, lineage, and metadata management is a plus.

Qualifications

• Bachelor/Master's degree in computer sciences, Engineering, or a related discipline, or equivalent industry experience.

Benefits & Perks

• Highly competitive compensation, benefits and rewards programs.
• An engaging, people-first work environment offering work/life balance, employee resource groups, and social events.
• Targeted base pay range for this role is $137,000 to $215,000, reflecting candidate knowledge, skills, and experience.