MLOps Engineer

Role Overview

The MLOps Engineer bridges machine learning research and production software engineering. You will own the infrastructure, pipelines, and automation needed to deploy, monitor, and reliably run models at scale, collaborating closely with data scientists and engineering teams to standardize the ML lifecycle.

Responsibilities

• Build and maintain automated CI/CD pipelines for ML models to move from development to production.
• Design and orchestrate ML workflows (e.g., Kubeflow, Airflow, Prefect) for training and evaluation.
• Implement model serving infrastructure using Triton Inference Server, TorchServe, or FastAPI on Kubernetes.
• Set up monitoring and alerting for model drift (model/concept), latency, and data quality.
• Collaborate on feature store and model registry to ensure reproducibility across offline/online.
• Optimize inference performance using quantization, pruning, and hardware-accelerated GPU execution.

Requirements

• 3–6 years experience in MLOps, DevOps, or software engineering focused on production ML.
• Proficiency with Docker, Kubernetes, and Helm charts.
• Hands-on cloud infrastructure experience (AWS or GCP) and Infrastructure as Code with Terraform.
• Strong Python skills.
• Familiarity with ML frameworks: PyTorch, TensorFlow, or Scikit-Learn.
• Experience integrating data pipelines and model registries such as MLflow or Weights & Biases.

Nice to Haves

• Experience with vector databases.
• Experience deploying LLMs.
• Experience working with Triton Inference Server.