MLOps Architect

We are seeking a visionary MLOps Architect to lead the design and implementation of scalable, secure, and high-performance MLOps frameworks across enterprise AI / ML initiatives.

This role is ideal for a seasoned technology leader with deep expertise in machine learning pipelines, DevOps best practices, and model lifecycle management using modern tools like MLflow, Feast, Docker, Kubernetes, and cloud platforms (AWS / GCP / Azure).

Key Responsibilities :

• Design and implement end-to-end MLOps architectures supporting robust and repeatable machine learning workflows.
• Build and maintain ML pipelines covering data ingestion, feature engineering, training, validation, deployment, and monitoring.
• Implement and manage feature stores using Feast, ensuring reuse, versioning, and governance of features across teams.
• Use MLflow for experiment tracking, model registry, and model packaging, enforcing standardization and reproducibility.
• Architect and deploy scalable model serving infrastructure (batch, streaming, real-time) using Docker, Kubernetes, and related tools.
• Set up comprehensive model monitoring systems for performance metrics, drift detection, and alerting to ensure model reliability in production.
• Champion CI / CD practices tailored for ML workflows using tools such as Jenkins, GitLab CI, or Azure DevOps.
• Apply Infrastructure as Code (IaC) principles using Terraform, CloudFormation, or similar tools for scalable, repeatable deployment.
• Collaborate with data scientists, engineers, and platform teams to streamline ML development, testing, and operationalization.
• Provide architectural guidance, code reviews, and best practices to cross-functional teams.

Required Qualifications & Skills :

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