Hitachi Digital Services is looking for an ML Ops Tech Lead to build and automate ML pipelines, operationalize models, manage cloud infrastructure, implement monitoring and observability, establish governance and security, and develop tooling and frameworks to accelerate the ML development process.
Requirements
- Experience: Overall 10+ years of experience with 4+ 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.
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: Design 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.
- Governance & Security: Establish and enforce best practices for model and data versioning, auditability, security, and access control across the entire machine learning lifecycle.
- Tooling & Frameworks: Develop and maintain reusable tools and frameworks to accelerate the ML development process and empower data science teams.
Other
- Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).
- We don’t expect you to ‘fit’ every requirement – your life experience, character, perspective, and passion for achieving great things in the world are equally as important to us.
- Championing diversity, equity, and inclusion (DEI) are integral to our culture and identity.
- We support your uniqueness and encourage people from all backgrounds to apply and realize their full potential as part of our team.
- We help take care of your today and tomorrow with industry-leading benefits, support, and services that look after your holistic health and wellbeing.
- We’re also champions of life balance and offer flexible arrangements that work for you (role and location dependent).
