Senior MLOps Engineer

Clariti

. Design and maintain end-to-end ML pipelines for training, evaluation, and deployment of models and agentic AI workflows .

Posted 10/15/2026full-timeRemote • United States, CanadaSenior💰 CA$150,000 - CA$200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining end-to-end ML pipelines, optimizing infrastructure for model training and serving, and implementing CI/CD best practices for ML systems. Proficient in deploying and managing ML models in production with a focus on performance, reliability, and observability.

Highest-signal resume keywords

MLOps EngineeringPython ProgrammingDocker and KubernetesCI/CD PipelinesCloud Platforms (AWS, GCP, Azure)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning InfrastructureModel DeploymentData VersioningExperiment TrackingPerformance MonitoringModel Optimization TechniquesData Pipeline DevelopmentAutomated RetrainingGPU OrchestrationPerformance Tuning

Soft Skills

Problem-SolvingCollaborative MindsetTeam-Oriented

Tools & Technologies

MLflowWeights & BiasesKubeflowGitHub ActionsJenkinsTerraformCloudFormation

Industry Keywords

End-to-End ML PipelinesModel ReproducibilityInfrastructure ProvisioningData QualityHigh-Performance Computing

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformJenkinsKubernetesPythonTerraformC++

About the role

Key responsibilities & impact

Design and maintain end-to-end ML pipelines for training, evaluation, and deployment of models and agentic AI workflows
Build and optimize infrastructure for training and model serving across cloud providers and compute instances
Develop tools for data pipelines, data labeling, experiment and performance tracking, and automated retraining
Collaborate with AI researchers and ML engineers to productionize POCs and ensure model reproducibility and scalability
Implement CI/CD best practices for ML systems, including continuous integration, automated testing, and deployment workflows
Monitor and manage model health, performance, drift, and data quality in production
Partner with Engineering teams to streamline infrastructure provisioning and data access
Drive cost optimization and performance tuning for large-scale model training
Contribute to internal documentation and best practices
Lead the development of robust ML infrastructure as the first MLOps Engineer, moving models efficiently from research to production with reliability, observability, and performance at scale

Requirements

What you’ll need

6–10+ years of experience in software or ML engineering, with at least 3+ in MLOps or ML infrastructure
Solid experience working with Python, C, C++, Bash, etc
Proven experience deploying and managing ML models in production
Proficiency with Docker and Kubernetes for scalable ML system design
Experience with cloud platforms (AWS, GCP, or Azure) and GPU orchestration
Hands-on knowledge of CI/CD pipelines (GitHub Actions, Jenkins, or similar)
Familiarity with MLflow, Weights & Biases, Kubeflow, and similar tools for experiment tracking and pipeline automation
Solid understanding of data versioning, model reproducibility, and monitoring strategies
Excellent problem-solving skills and a collaborative, team-oriented mindset
Successful candidates must undergo comprehensive background checks before joining the team
Bonus: Experience training models from scratch, including defining architectures, curating and cleaning datasets, tuning training parameters, and bringing models from research to monitored production
Bonus: Exposure to model optimization techniques (quantization, distillation, TensorRT, ONNX)
Bonus: Familiarity with infrastructure-as-code tools (Terraform, CloudFormation)
Bonus: Background in distributed systems or high-performance computing
Bonus: Contributions to open-source projects

Benefits

Comp & perks

Competitive compensation packages
Time off
Benefits to keep you and your family healthy
Occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year
Inclusive culture and support for accommodations during the hiring process