MLOps Engineer

About The Role

The role sits at the intersection of ML and platform engineering, owning the infrastructure that takes models from a data scientist's notebook to reliable, monitored, cost-efficient production systems serving real traffic.

This is a high-leverage position: the MLOps engineer builds the CI/CD pipelines, feature stores, model registries, and observability tooling that every ML team depends on, directly determining how fast the organization ships and how reliably its models run.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workloads using tools like GitHub Actions, GitLab CI, or Airflow, with automated testing and staged rollout gates for model releases
• Build and operate model serving infrastructure on Kubernetes (EKS/GKE) using KServe, Seldon, or Triton Inference Server, optimizing for latency, autoscaling, and GPU utilization
• Implement model monitoring and observability: data drift detection, latency/throughput dashboards, and automated alerting via Prometheus, Grafana, and Evidently
• Establish model versioning and lineage using MLflow or a comparable model registry, ensuring reproducibility of training runs and clean promotion workflows from staging to production
• Engineer scalable data and feature pipelines with Spark, Kafka, or dbt, coordinating with data engineering on feature store architecture (Feast, Tecton, or custom)
• Automate infrastructure as code with Terraform and manage ML platform components across AWS, GCP, or Azure
• Drive cost optimization across training and inference workloads, including spot instance strategies, batching, and GPU right-sizing

What We Are Looking For

• 3–7 years of experience in MLOps, ML engineering, or platform/SRE roles supporting ML workloads in production
• Strong Python and software engineering fundamentals; comfort owning services end-to-end, not just glue code
• Deep hands-on experience with Kubernetes, Docker, and at least one major cloud (AWS, GCP, or Azure)
• Production experience with model serving and orchestration tools (MLflow, KServe, Kubeflow, Airflow, or SageMaker Pipelines)
• Solid grasp of ML fundamentals sufficient to reason about training pipelines, model evaluation, and drift — no need to build models from scratch, but must speak the language
• Experience with IaC (Terraform), observability stacks (Prometheus/Grafana/Datadog), and incident response practices
• Bachelor's degree in CS, Engineering, or equivalent practical experience. Bonus: experience with GPU infrastructure and inference optimization (vLLM, TensorRT, quantization), feature stores at scale, or building internal ML platforms used by multiple teams