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