Description

You will architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring.

Responsibilities

• Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry.
• Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar.
• Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML.
• Partner with Data Engineers to build robust data pipelines for model-ready datasets.
• Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions.

Required Skills

• 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure.
• Expertise in building large-scale distributed systems and microservices.
• Strong programming skills in Python, Go, or Java.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks.
• Cloud platform experience (AWS, GCP, or Azure).
• Experience with statistical learning algorithms (GLM, XGBoost, Random Forest) and deep learning (neural networks, transformers).
• Knowledge of Data Scientists and MLOps practices.

Key Skills

Education

Bachelor's degree