Job Description:
Design, implement, and enhance robust and scalable infrastructure that enables efficient deployment, monitoring, and management of machine learning models in production. In this role, you will bridge the gap between research and production environments, streamline data and feature pipelines, optimize model serving, and ensure governance and reproducibility across our ML lifecycle.
Additional Positions:
Job Qualifications:
3+ years of experience as an MLOps, ML Infrastructure, or Software Engineer in ML-driven environments, preferably with PyTorch.
Strong proficiency in Python, SQL (leveraging platforms like Snowflake and RDS), and distributed computing frameworks (e.g., Dask, Spark) for processing large-scale data in formats like Parquet.
Hands-on experience with feature stores, key-value stores like Redis, MLflow (or similar tools), Kubernetes, Docker, cloud infrastructure (AWS, specifically S3 and EC2), and orchestration tools (Airflow).
Proven ability to build and maintain scalable and version-controlled data pipelines, including real-time streaming with tools like Kafka.
Experience in designing and deploying robust ML serving infrastructures with CI/CD automation.
Familiarity with monitoring tools and practices for ML systems, including drift detection and model performance evaluation.
Company Occupation:
High Tech
Company Size:
Medium (50 - 150)