Description

Build and maintain the core ML infrastructure that supports the end-to-end machine learning lifecycle, from data preparation and training to deployment and monitoring.

This role is on-site.

Responsibilities

• Design and implement scalable ML platforms using cloud-native and open-source technologies.
• Orchestrate ML workflows and ensure reproducibility using tools like Kubeflow, SageMaker, MLflow, or Databricks.
• Partner with Data Engineers to build reliable data pipelines and feature pipelines for model-ready datasets.
• Implement CI/CD for ML, including automated testing, validation, and safe rollout of models.
• Embed observability and governance into the platform, including logging, tracing, and drift detection.

Required Skills

• 6+ years of software engineering experience, including 3+ years on ML platforms or infrastructure.
• Strong programming skills in Python, Java, or Go.
• Hands-on experience with Docker and Kubernetes for containerization and orchestration.
• Experience with cloud platforms (AWS, GCP, or Azure), including compute, storage, and networking.
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, or Databricks.
• Understanding of distributed systems, microservices, and system design.
• Practical knowledge of statistical learning (GLM, XGBoost) and deep learning (neural networks, transformers).
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Preferred Skills

• Experience with real-time model inference, streaming pipelines, and feature stores (e.g., Feast, Tecton).
• Familiarity with infrastructure-as-code (Terraform) and CI/CD tools (TeamCity).

Key Skills

Education

Bachelor's or Master's degrees