Tech Lead – MLOps, Infrastructure

MegazoneCloud US

. Own the design and implementation of production-grade ML pipelines .

Posted 9/15/2026contractRemote • United StatesSenior💰 $80 - $120 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing production-grade ML pipelines and MLOps practices, including CI/CD processes, infrastructure-as-code with Terraform, and compliance with 21 CFR Part 11. Proven ability to lead technical teams, mentor peers, and coordinate with cross-functional stakeholders to ensure robust and scalable ML solutions.

Highest-signal resume keywords

MLOps LeadershipProduction-Grade ML Pipeline DesignTerraform Infrastructure DesignAmazon SageMaker Automation21 CFR Part 11 Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML Pipeline ImplementationCI/CD for ML ArtifactsModel Performance MonitoringHyperparameter TuningData Drift DetectionContainer BuildsIntegration TestingModel RegistriesInfrastructure MonitoringAuto-Scaling

Soft Skills

Technical Decision-MakingMentoringCollaboration

Tools & Technologies

Amazon SageMakerTerraform

Industry Keywords

MLOps Best PracticesGovernanceReproducibilityAudit TrailsQuality Assurance

Tech Stack

Tools & technologies

Terraform

About the role

Key responsibilities & impact

Own the design and implementation of production-grade ML pipelines
Lead architecture and implementation of end-to-end MLOps pipelines, including CI/CD for training, evaluation, approval, and deployment with audit trails
Design and deploy Terraform infrastructure for ML platform resources
Build automated Amazon SageMaker training jobs for life sciences workloads
Implement model performance monitoring and automated retraining triggers
Establish CI/CD for ML artifacts, including versioning, container builds, integration testing, and staged rollouts with validation gates
Design model registries and artifact management for governance, reproducibility, and 21 CFR Part 11 compliance
Implement monitoring, alerting, and auto-scaling for training and inference workloads
Define and enforce MLOps best practices, coding standards, and architectural patterns
Serve as overall Tech Lead through architecture reviews, mentoring, and technical decision-making
Coordinate with customer platform, IT security, and quality teams on networking, security, and compliance

Requirements

What you’ll need

Technical leadership experience in MLOps and infrastructure
Experience designing and implementing production-grade ML pipelines
Experience with infrastructure-as-code using Terraform
Experience building automated training jobs on Amazon SageMaker
Knowledge of hyperparameter tuning, distributed training, and spot optimization
Experience implementing model performance monitoring, data drift detection, prediction quality tracking, and automated retraining triggers
Experience with CI/CD for ML artifacts, model versioning, container builds, integration testing, and staged rollouts
Knowledge of model registries and artifact management for governance and reproducibility
Knowledge of 21 CFR Part 11 compliance
Experience with infrastructure monitoring, alerting, and auto-scaling
Ability to define and enforce MLOps best practices, coding standards, and architectural patterns
Ability to conduct architecture reviews, mentor, and make technical decisions
Ability to coordinate with platform, IT security, and quality teams
Must be eligible to work in the United States for any employer
Must not require employment visa sponsorship

Benefits

Comp & perks

Growth and professional development investment
Servant leadership and management support
Flat organization with direct impact on technical roadmap and client success
Learn-from-it culture treating mistakes as learning opportunities
Equal employment opportunity employer