Machine Learning Engineer – Platform

Machine Learning Engineer – Platform

Requirements

• 2+ years of experience in a Machine Learning Platform, MLOps, or Data Engineering role, or strong internship/project experience in the space.
• Familiarity with core MLOps concepts such as automated model training/deployment, monitoring, and experiment tracking.
• Proficiency in Python and common ML/DS libraries (e.g., scikit-learn, pandas, MLflow).
• Exposure to cloud platforms (e.g., AWS, GCP, or Azure) and tools like Docker, Kubernetes, or Terraform.
• Experience with data engineering and analytics platforms like Databricks.
• Understanding of distributed data processing with Spark is a plus.
• Familiarity with observability and monitoring tools such as Datadog is a plus.
• A strong desire to learn and grow within the ML infrastructure and MLOps domain.
• Bachelor’s or advanced degree in Computer Science, Data Science, Engineering, or a related field.

Responsibilities

• Collaborate with senior engineers and data scientists to design, build, and improve components of our MLOps stack, including model training pipelines, model serving infrastructure, feature stores, and model monitoring systems.
• Assist in the development of scalable and reproducible ML workflows using tools such as Airflow, MLflow, or similar orchestration and experiment tracking systems.
• Support the automation of model deployment and lifecycle management via CI/CD pipelines, containerization, and infrastructure-as-code.
• Help maintain a reliable ML platform through performance tuning, logging, alerting, and observability practices.
• Stay current with trends in ML infrastructure and tools, contributing ideas to improve our platform’s capabilities and efficiency.

Preferred Qualifications

• Understanding of distributed data processing with Spark is a plus.
• Familiarity with observability and monitoring tools such as Datadog is a plus.