MLOps Engineer

About The Role The role owns the infrastructure and pipelines that get machine learning models from research notebooks into reliable, scalable production systems. This means building CI/CD for ML, designing serving architectures that handle real traffic, and making sure training, deployment, and monitoring all run without someone manually babysitting a terminal.

The team sits at the intersection of data science and platform engineering - working directly with ML engineers and data scientists whose models depend on the reliability of what gets built here. The problems are concrete: GPU utilization, feature pipelines that don’t silently break, deployments that roll back cleanly, and costs that don’t spiral out of control.

Key Responsibilities

• Build and maintain end-to-end MLOps pipelines covering data ingestion, feature engineering, training, evaluation, and deployment using tools like Airflow, Kubeflow, or Vertex AI Pipelines
• Design CI/CD workflows for ML systems - automated testing for models and data, staged rollouts, canary deployments, and one-click rollback mechanisms
• Deploy and scale model serving infrastructure using Docker, Kubernetes, and frameworks like TorchServe, KServe, or Triton Inference Server
• Implement production monitoring for model performance, data drift, and system health using tools like Prometheus, Grafana, Evidently, or Arize
• Manage GPU infrastructure and training workloads - optimize utilization, batching strategies, and distributed training setups (multi-node, multi-GPU)
• Establish model versioning and experiment tracking standards using MLflow, Weights & Biases, or equivalent tools across the ML organization
• Partner with data scientists and ML engineers to debug production issues, harden pipelines, and reduce inference latency and infrastructure costs

What We Are Looking For

• 3–7 years of experience in MLOps, ML engineering, DevOps for ML, or backend engineering with heavy ML infrastructure exposure
• Strong Python and SQL skills, plus fluency in at least one compiled or systems language (Go, Rust, or C++)
• Deep hands-on experience with Kubernetes, Docker, and at least one major cloud platform (GCP, AWS, or Azure)
• Production experience with ML deployment and serving patterns: batch, real-time, streaming, or LLM inference
• Experience building or operating data/feature pipelines with tools like Airflow, dbt, Spark, or Kafka
• Solid grasp of ML fundamentals enough to reason about models, metrics, and failure modes - not just infrastructure
• Bonus: Experience with LLM serving and optimization (vLLM, TensorRT-LLM, quantization), infrastructure-as-code (Terraform), or fine-tuning pipeline orchestration; BS/MS in Computer Science or related field