MLOps Engineer

We are seeking an experienced MLOps Engineer for one of our project, to drive the optimization and scalability of machine learning infrastructure. You will play a critical role in building and maintaining robust ML pipelines, ensuring seamless model deployment, and implementing best practices for ML operations at scale.

Key Responsibilities
 

Model Deployment & Infrastructure

• Optimize Amazon SageMaker model deployments for performance, cost-efficiency, and reliability
• Design and implement auto-scaling strategies for ML inference endpoints to handle varying traffic loads
• Build and maintain AWS-based ML infrastructure using best practices for security, monitoring, and cost optimization

CI/CD & Automation

• Enhance and maintain GitHub Actions workflows for automated ML model building, testing, and deployment
• Implement automated quality gates and validation checks in the ML deployment pipeline
• Develop infrastructure-as-code solutions for consistent and reproducible ML environments

Inference Optimization

• Research, experiment with, and implement various inference methods including:
  • Asynchronous inference for high-throughput scenarios
  • Batch inference for large-scale data processing
  • Real-time inference optimization
• Analyze and optimize inference latency, throughput, and cost trade-offs

Training & Model Lifecycle Management

• Design and implement automated training and re-training pipelines
• Build systems for continuous model improvement based on new data and performance metrics
• Manage model versioning, artifact storage, and deployment rollback strategies

Model Monitoring & Quality Assurance

• Implement comprehensive model evaluation frameworks and automated testing
• Set up model drift detection systems to monitor data and concept drift
• Build alerting and monitoring systems for model performance degradation
• Develop automated model validation and A/B testing capabilities
  
  Required Qualifications
   
• Experience: 3+ years of hands-on experience in MLOps, DevOps, or related fields
• Cloud Platforms: Strong expertise with AWS services, particularly Amazon SageMaker, EC2, S3, Lambda, and CloudWatch
• Programming: Proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn)
• CI/CD: Hands-on experience with GitHub Actions, Docker, and containerization technologies
• Infrastructure: Experience with Infrastructure as Code tools (Terraform, CloudFormation, or CDK)
• ML Lifecycle: Understanding of the complete ML lifecycle from data ingestion to model deployment and monitoring
  
  Technical Skills
   
• Cloud Services: AWS SageMaker, EC2, S3, Lambda, CloudWatch, IAM, VPC
• Containerization: Docker, Kubernetes, Amazon EKS
• Programming Languages: Python, Bash/Shell scripting
• ML Tools: MLflow, Weights & Biases, Amazon SageMaker Studio
• CI/CD Tools: GitHub Actions, Jenkins, GitLab CI
• Infrastructure: Terraform, AWS CloudFormation, AWS CDK
• Monitoring: CloudWatch, Prometheus, Grafana
• Databases: Experience with both SQL and NoSQL databases