Description

•          Experiment-to-Production: Own the path from model experimentation to reliable production deployment.

•          ML Infrastructure: Build and maintain the infrastructure, pipelines, and automation that let models deploy efficiently and reliably.

•          Containerization & Orchestration: Package and orchestrate workloads using Docker and Kubernetes.

•          CI/CD for ML: Design and operate CI/CD workflows for training, packaging, and deploying models.

•          Experiment Tracking & Versioning: Implement experiment tracking and model/feature versioning so results are reproducible.

•          Monitoring & Observability: Build dashboards, drift detection, and production monitoring to keep systems stable and performant.

•          GPU & Compute Management: Provision and optimize GPU compute and cloud resources for training and inference.

•          Cost & Scale: Make deployments reproducible, scalable, observable, and cost-effective.

•          Collaboration: Work closely with model developers, applying AI/ML understanding to streamline their path to production.

Qualifications

Qualifications we’re looking for.

Experience

•          3–5 years of experience in MLOps, ML platform, or ML/AI infrastructure engineering.

•          Proven track record deploying and operating models in production.

Skills

•          Experience with cloud environments (Azure) and GPU compute.

•          Strong containerization and orchestration skills (Docker, Kubernetes).

•          Experience building CI/CD workflows for ML or software systems.

•          Hands-on experience with experiment tracking and model versioning (e.g. MLflow).

•          Experience building dashboards, drift detection, and production monitoring.

•          Strong Python skills and familiarity with infrastructure-as-code and automation.

Abilities

•          Sufficient understanding of AI/ML concepts to work closely with model developers.

•          Able to make deployments reproducible, scalable, observable, and cost-effective.

•          Strong problem-solving skills and ability to work in a fast-paced, agile environment.

Education

•          Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Preferred Qualifications

•          Experience with ML pipeline / orchestration tools (Kubeflow, Airflow, Azure ML Pipelines, or Vertex AI).

•          Experience optimizing inference (quantization, ONNX, TensorRT) and edge or real-time deployment.

•          Familiarity with observability tooling (Prometheus, Grafana, ELK) and model performance monitoring.

•          Experience with feature stores, data versioning, and reproducible data pipelines.

•          Exposure to retail, IoT, or computer vision production systems

Key Skills

Education

Bachelor's or Master's degrees