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Megazone Cloud US is seeking a Tech Lead for MLOps & Infrastructure to own the design and implementation of production-grade ML pipelines, infrastructure-as-code automation, and model lifecycle management. This is an overall Tech Lead role that drives architectural decisions, sets technical standards, and ensures the ML platform is reliable, scalable, and compliant with operational and regulatory requirements—particularly validation and reproducibility standards for healthcare and life sciences AI applications.
Key Responsibilities:
- Architect and implement end-to-end MLOps pipelines with CI/CD for model training, evaluation, approval, and deployment, including comprehensive audit trails
- Design and deploy infrastructure-as-code using Terraform for all ML platform resources
- Build automated training jobs on Amazon SageMaker, including hyperparameter tuning, distributed training, and spot optimization tailored for life sciences workloads
- Implement model performance monitoring systems: data drift detection, prediction quality tracking, and automated retraining triggers
- Establish CI/CD for ML artifacts: model versioning, container builds, integration testing, and staged rollouts with validation gates
- Design model registry and artifact management systems to ensure governance, reproducibility, and 21 CFR Part 11 compliance
- Implement infrastructure monitoring, alerting, and auto-scaling for both training and inference workloads
- Define and enforce MLOps best practices, coding standards, and architectural patterns across the team
- Serve as overall Tech Lead: conduct architecture reviews, mentor team members, and make technical decisions
- Coordinate with customer platform, IT security, and quality teams on networking, security, and compliance requirements
The company emphasizes servant leadership, a flat organizational structure with real impact, and a learning-oriented culture. You'll work on cutting-edge technology to solve real problems for healthcare and life sciences customers.
Requirements:
The posting does not explicitly state years of experience or specific technical certifications required. However, the role expects expertise in MLOps architecture, Terraform/infrastructure-as-code, Amazon SageMaker, CI/CD pipelines, model governance, and regulatory compliance (21 CFR Part 11). Experience with healthcare/life sciences AI, data drift detection, model monitoring, and distributed training systems is implied by the domain focus.
