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 Data quality and drift detection Service reliability and uptime Infrastructure utilization Latency and throughput metrics Develop operational dashboards, alerts, incident response processes, and runbooks. AWS Cloud Engineering Design and manage AI solutions leveraging AWS services including: Amazon SageMaker S3 ECR ECS/EKS AWS Lambda Step Functions CloudWatch IAM KMS Secrets Manager Implement cloud security best practices, role-based access controls, and encryption standards. Dataiku Platform Integration Configure and integrate Dataiku with enterprise datasets and AWS services. Support governed machine learning deployment and automation workflows. Enable seamless model promotion and monitoring across development and production environments. Governance, Risk & Compliance Ensure compliance with data privacy, security, Responsible AI, and model governance requirements. Maintain model lineage, audit trails, approvals, and technical documentation. Implement best practices for model risk management and regulatory compliance. Performance & Cost Optimization Optimize AI infrastructure for performance, resilience, scalability, and cost efficiency. Implement FinOps practices including resource tagging, utilization monitoring, capacity planning, and cost tracking. Continuously improve platform efficiency and operational effectiveness. Collaboration & Technical Leadership Partner with Data Scientists and ML Engineers to productionize machine learning solutions. Support Data Engineering and Platform teams in integrating AI solutions within enterprise environments. Provide technical guidance, conduct code reviews, and promote MLOps best practices. Contribute to continuous improvement initiatives across AI and data platforms. Requirements Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Technology, Engineering, or a related discipline. Master's degree in a relevant technical field is preferred. Minimum 7+ years of experience in MLOps, Machine Learning Engineering, DevOps, Platform Engineering, Cloud Engineering, or Software Engineering. Proven experience deploying and managing machine learning solutions in production environments. Strong hands-on experience with AWS and Amazon SageMaker. Benefits Gratuity Annual Leaves Health Insurance Annual Air ticket Visa Sponsorship