Machine Learning Engineer

Role Overview

Build and scale high-performance machine learning models from prototype to production. You’ll work with Data Science and Software Engineering teams to design, implement, and maintain robust ML pipelines and model architectures for real-time decision-making.

Responsibilities

• Design and implement scalable machine learning models and training pipelines using PyTorch, TensorFlow, or scikit-learn.
• Develop, optimize, and maintain real-time feature stores and data pipelines in Python and SQL.
• Deploy ML models to production cloud environments using Docker and Kubernetes.
• Build automated MLOps pipelines for continuous training, testing, deployment, and monitoring (e.g., feature drift and performance degradation).
• Collaborate with backend engineers to integrate model endpoints into high-throughput, low-latency APIs and microservices.
• Write clean, maintainable, highly optimized code; participate in architecture discussions and peer code reviews.

Requirements

• 3–6 years of experience as an ML Engineer or Software Engineer focused on productionizing ML models.
• Strong Python skills and familiarity with ML frameworks (PyTorch, TensorFlow, or XGBoost).
• Solid cloud experience (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
• Strong understanding of modern MLOps including CI/CD for ML, MLflow and/or Weights & Biases, and feature stores.
• Bachelor’s or Master’s degree in a technical field (Computer Science, Data Science, Mathematics, or related).

Nice to Have

• Experience with LLMs and RAG (Retrieval-Augmented Generation) pipelines.
• Experience with distributed data processing frameworks such as PySpark.