Tech Lead – MLOps, Infrastructure

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of the design and execution of production-grade machine learning pipelines.

• Lead the architecture and implementation of comprehensive MLOps pipelines, including continuous integration and continuous deployment (CI/CD) for training, evaluation, approval, and deployment, complete with audit trails.

• Design and implement Terraform infrastructure for resources on the machine learning platform.

• Create automated training jobs using Amazon SageMaker for life sciences applications.

• Set up model performance monitoring and automated retraining triggers.

• Establish CI/CD processes for machine learning artifacts, which encompass versioning, container builds, integration testing, and staged rollouts with validation gates.

• Develop model registries and manage artifacts to ensure governance, reproducibility, and compliance with 21 CFR Part 11.

• Implement monitoring, alerting, and auto-scaling solutions for both training and inference workloads.

• Define and uphold MLOps best practices, coding standards, and architectural patterns.

• Act as the overall Tech Lead through architecture evaluations, mentorship, and technical decision-making.

• Collaborate with customer platform, IT security, and quality assurance teams to address networking, security, and compliance concerns.

⛳️ Requirements

• Demonstrated technical leadership experience in MLOps and infrastructure.

• Proven experience in designing and implementing production-quality ML pipelines.

• Proficiency in infrastructure-as-code practices using Terraform.

• Experience in developing automated training jobs on Amazon SageMaker.

• Familiarity with hyperparameter tuning, distributed training, and spot optimization techniques.

• Experience in implementing model performance monitoring, data drift detection, prediction quality tracking, and automated retraining mechanisms.

• Knowledge of CI/CD practices for ML artifacts, including model versioning, container builds, integration testing, and staged rollouts.

• Understanding of model registries and artifact management for governance and reproducibility.

• Familiarity with 21 CFR Part 11 compliance requirements.

• Experience with monitoring, alerting, and auto-scaling of infrastructure.

• Ability to define and enforce MLOps best practices, coding standards, and architectural patterns.

• Capability to conduct architecture reviews, provide mentorship, and make technical decisions.

• Ability to coordinate efforts with platform, IT security, and quality assurance teams.

• Must be eligible to work in the United States for any employer.

• Must not require sponsorship for an employment visa.

🏝️ Benefits

• Investment in growth and professional development.

• Support through servant leadership and management.

• A flat organizational structure that allows for direct influence on the technical roadmap and client success.

• A culture that emphasizes learning from mistakes and treating them as opportunities for growth.

• An equal employment opportunity employer.