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.

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.