Role overview
You’ll work alongside Data Science, Platform Engineering, and Software Engineering to design the infrastructure, tooling, and automation that powers their ML lifecycle. This is a senior technical contributor role with ownership, autonomy, and influence on architecture and roadmap.
Responsibilities
- Build and maintain end‑to‑end ML pipelines (training, deployment, monitoring)
- Develop scalable model‑serving systems across batch and real‑time use cases
- Implement CI/CD workflows for ML
- Set standards for observability, reliability, and model governance
- Automate retraining and model promotion workflows
- Collaborate across teams to improve platform performance and engineering velocity
Basic qualifications
- Strong Python engineering background
- Hands‑on experience with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure)
- Experience with ML workflow tools (e.g., MLflow, Kubeflow, SageMaker, Vertex, Airflow, Dagster, Prefect)
- Strong understanding of model deployment, distributed systems, and data pipelines
- Practical experience building production ML systems
Preferred qualifications
- Familiarity with feature stores or model registries
- Monitoring/observability tooling
- Streaming platforms such as Kafka or Kinesis
- Terraform or other IaC tools
- Experience with LLM/GenAI‑related pipelines
Tags & focus areas
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