MLOps Engineer

About The Role

The role owns the infrastructure that turns experimental ML work into reliable production systems - CI/CD for models, scalable training infrastructure, real-time and batch serving, and the observability tooling that keeps it all running.

You will work directly with ML engineers and data scientists to eliminate the friction between 'it works in a notebook' and 'it works at scale', owning the platform decisions that determine how fast models ship and how reliably they perform.

Key Responsibilities

Design and maintain CI/CD pipelines for ML workflows using tools like Kubeflow, MLflow, or Argo Workflows, from model versioning through automated deployment Build and operate model serving infrastructure on Kubernetes, including GPU scheduling, autoscaling, canary deployments, and rollback strategies Implement monitoring and observability for production models: latency, throughput, data drift detection, and automated alerting with tools like Prometheus, Grafana, and Evidently Develop feature stores and data pipeline integrations (Airflow, dbt, Spark) ensuring consistency between training and serving environments Optimize training and inference costs across cloud providers (AWS, GCP, or Azure), including GPU utilization, spot instance strategies, and model quantization where appropriate Establish MLOps best practices across teams: model registries, experiment tracking, reproducibility standards, and infrastructure-as-code with Terraform Partner with ML engineers and data scientists to debug production incidents, profile performance bottlenecks, and improve deployment velocity What We Are Looking For 3-6 years of experience in MLOps, DevOps, or platform engineering, with at least 2 years supporting ML systems in production Strong proficiency with Kubernetes, Docker, and cloud infrastructure (AWS, GCP, or Azure) in production environments Hands-on experience with ML tooling: MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent model deployment and tracking platforms Solid software engineering skills in Python and/or Go, including infrastructure-as-code experience with Terraform or Pulumi Experience building observability for distributed systems: metrics, logging, tracing, and drift monitoring for ML workloads Bachelor's degree in Computer Science, Engineering, or equivalent practical experience Bonus: Experience with GPU scheduling (NVIDIA device plugins, Slurm), LLM inference optimization (vLLM, TensorRT), or building internal ML platforms used by multiple teams