Job Responsibilities

• Own and drive the enterprise MLOps and data platform architecture, enabling scalable ML workloads and data pipelines across business units
• Lead development of enterprise-wide MLOps and Generative AI frameworks, standardizing ML lifecycle, deployment, and governance practices
• Drive DataOps and feature engineering initiatives, managing large-scale data pipelines from ingestion to model serving
• Design and implement end-to-end ML systems including training, validation, deployment, monitoring, and retraining workflows
• Lead DevOps practices and CI/CD implementation for ML and data platforms, ensuring reliable and automated deployments
• Own Infrastructure as Code (IaC) strategy using Terraform, Terragrunt, and CloudFormation across multi-account cloud environments
• Establish and enforce security, governance, and compliance frameworks including IAM, RBAC, encryption, and auditability
• Build and maintain observability frameworks for monitoring, logging, model performance, and system reliability
• Enable and scale self-service ML platforms, improving developer productivity and reducing deployment timelines
• Drive cloud cost optimization (FinOps) and operational efficiency across ML and data workloads
• Lead cross-functional collaboration with data science, engineering, and business teams to deliver production-ready AI solutions
• Evaluate and integrate emerging technologies, including Generative AI (LLMs, RAG, multi-model systems), into enterprise platforms

Job Requirements

• Bachelor’s degree in Computer Science, Engineering, or related field
• 6+ years of experience in MLOps, DevOps, Platform Engineering, or Data Engineering
• Strong experience building and operating enterprise-scale ML platforms
• Hands-on experience with AWS (preferred) or other cloud platforms
• Strong experience with CI/CD tools (Jenkins, GitHub Actions, etc.)
• Experience with Infrastructure as Code (Terraform, Terragrunt, CloudFormation)
• Experience with containerization and orchestration (Docker, Kubernetes)
• Strong understanding of ML lifecycle management and MLOps tools (MLflow, SageMaker, Databricks, etc.)
• Experience with data engineering systems (ETL/ELT pipelines, feature stores, large-scale data processing)
• Experience implementing observability and monitoring frameworks
• Strong understanding of security and governance in cloud and ML systems
   

Preferred Qualifications

• Experience building multi-tenant, enterprise ML platforms
• Experience with Databricks, feature stores, and large-scale distributed data systems
• Strong programming skills (Python preferred)
• Experience handling petabyte-scale data pipelines
• Experience in multi-account AWS environments and platform standardization
• Exposure to Generative AI frameworks (LLMs, RAG, vector databases)
• Experience in manufacturing or industrial domain environments
   

Key Competencies

• Strong system design and architecture mindset
• Ability to build platforms, not just pipelines
• Ownership of large-scale, cross-team engineering initiatives
• Strong collaboration and stakeholder communication skills
• Focus on scalability, reliability, and governance

Competencies