MLOps Engineer

We are seeking a highly skilled MLOps Engineer to design, build, and operate secure, scalable, and governed Machine Learning Operations (MLOps) capabilities that enable the reliable deployment and lifecycle management of AI and Machine Learning models. The role will focus on establishing production-grade ML platforms, automating model deployment pipelines, managing cloud-based AI infrastructure, and ensuring operational excellence across the AI ecosystem, with a primary emphasis on AWS technologies.

The successful candidate will work closely with Data Scientists, Data Engineers, Platform Engineering teams, Information Security, Architecture, and business stakeholders to accelerate AI delivery while maintaining governance, reliability, observability, cost efficiency, and regulatory compliance.

Key Responsibilities

MLOps Architecture & Platform Engineering

• Design and implement scalable MLOps architectures and reusable deployment patterns across development, testing, staging, and production environments.

• Establish enterprise-wide standards for machine learning deployment, automation, monitoring, and operational management.

• Develop robust and secure AI platforms capable of supporting large-scale machine learning workloads.

End-to-End Machine Learning Lifecycle Management

• Build and maintain automated ML workflows covering data preparation, experimentation, model training, validation, approval, deployment, monitoring, and retraining.

• Implement model registries, experiment tracking, and governance processes to ensure traceability and reproducibility.

• Support the complete AI model lifecycle from development to production and retirement.

CI/CD & Infrastructure Automation

• Implement CI/CD pipelines for machine learning applications and infrastructure using Git-based workflows.

• Develop Infrastructure as Code (IaC) solutions using Terraform, AWS CloudFormation, or equivalent technologies.

• Automate testing, deployment, validation, and release management processes.

Model Deployment & Production Operations

• Deploy machine learning models for both batch and real-time inference use cases.

• Implement model serving solutions using containerized, serverless, and Kubernetes-based deployment architectures.

• Ensure version control, rollback mechanisms, high availability, and scalability of deployed models.

Monitoring, Observability & Reliability

• Establish comprehensive monitoring and observability frameworks for:
• Model performance