Senior MLOps Engineer

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Design and oversee comprehensive ML pipelines for the training, evaluation, and deployment of models and agentic AI workflows.

• Construct and enhance infrastructure for model training and serving across various cloud providers and compute instances.

• Create tools for data pipelines, data labeling, experiment tracking, performance monitoring, and automated retraining.

• Collaborate with AI researchers and ML engineers to transition POCs to production while ensuring model reproducibility and scalability.

• Apply 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 environments.

• Work in partnership with Engineering teams to optimize infrastructure provisioning and data accessibility.

• Lead initiatives for cost optimization and performance tuning in large-scale model training.

• Contribute to internal documentation and establish best practices.

• Spearhead the development of robust ML infrastructure as the inaugural MLOps Engineer, ensuring seamless transition of models from research to production with reliability, observability, and performance at scale.

⛳️ Requirements

• 6–10+ years of experience in software or ML engineering, with a minimum of 3+ years focused on MLOps or ML infrastructure.

• Strong experience with programming languages such as Python, C, C++, Bash, etc.

• Demonstrated experience in deploying and managing ML models in a production setting.

• Proficiency in using Docker and Kubernetes for scalable ML system architecture.

• Familiarity with cloud platforms (AWS, GCP, or Azure) and GPU orchestration.

• Practical knowledge of CI/CD pipelines (GitHub Actions, Jenkins, or similar tools).

• Experience with MLflow, Weights & Biases, Kubeflow, and similar tools for experiment tracking and pipeline automation.

• Strong understanding of data versioning, model reproducibility, and monitoring strategies.

• Exceptional problem-solving abilities and a collaborative, team-oriented approach.

• Candidates must successfully complete comprehensive background checks prior to joining the team.

• Bonus: Experience in training models from scratch, including defining architectures, curating and cleaning datasets, tuning training parameters, and transitioning models from research to monitored production.

• Bonus: Familiarity with model optimization techniques (quantization, distillation, TensorRT, ONNX).

• Bonus: Knowledge of infrastructure-as-code tools (Terraform, CloudFormation).

• Bonus: Background in distributed systems or high-performance computing.

• Bonus: Contributions to open-source projects.

🏝️ Benefits

• Competitive compensation packages.

• Generous time off.

• Comprehensive benefits to promote health for you and your family.

• Occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year.

• An inclusive culture with support for accommodations during the hiring process.