Machine learning operations (MLOps)

Responsibilities

Build & Automate ML Pipelines: Design, implement, and maintain CI/CD pipelines for machine learning models, ensuring automated data ingestion, model training, testing, versioning, and deployment.
Operationalize Models: Collaborate closely with data scientists to containerize, optimize, and deploy their models to production, focusing on reproducibility, scalability, and performance.
Infrastructure Management: build and manage the underlying cloud infrastructure (AWS) that powers our MLOps platform, leveraging Infrastructure-as-Code (IaC) tools to ensure consistency and cost optimization.
Monitoring & Observability: Implement comprehensive monitoring, alerting, and logging solutions to track model performance, data integrity, and pipeline health in real-time. Proactively address issues like model or data drift.
Tooling & Frameworks: Develop and maintain reusable tools and frameworks to accelerate the ML development process and empower data science teams.

Required Qualifications

Experience: Overall 5+ years of experience with 2+ years of experience in MLOps, Machine Learning Engineering, or a related DevOps role with a focus on ML workflows.
Cloud Expertise: Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker, S3, ECS, EKS, Lambda, SQS, SNS, and IAM.
Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools, including Terraform for IaC and GitHub Actions.
MLOps & DevOps: A solid understanding of MLOps and DevOps principles. Hands-on experience with MLOps frameworks like Sagemaker Pipelines, Model Registry, Weights and Bias, MLflow or Kubeflow and orchestration tools like Airflow or Argo Workflows.
Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
Model Lifecycle: Experience with model testing, validation, and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required to effectively collaborate with data scientists.
Communication: Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).