Machine Learning Engineer

About The Role

The role owns the end-to-end design, implementation, and scaling of machine learning systems powering high-throughput enterprise applications.

The team works at the intersection of applied research and platform engineering, building resilient infrastructure that ensures models operate reliably at production scale.

Key Responsibilities

• Architect and deploy production-grade machine learning models using Python, PyTorch, and cloud infrastructure on AWS
• Build robust data ingestion and feature engineering pipelines using Apache Spark and SQL to support continuous model training
• Optimize model serving architectures for low latency and high availability, utilizing containerization tools like Docker and Kubernetes
• Implement comprehensive monitoring systems to track data drift, concept drift, and system performance regressions in real time
• Collaborate with backend engineers and product teams to integrate machine learning capabilities seamlessly into core software services
• Contribute to internal engineering standards by writing clean, well-tested code and participating in rigorous peer code reviews

What We Are Looking For

• 3 to 6 years of professional experience in machine learning engineering or applied software development
• Strong proficiency in Python and hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow
• Demonstrated track record of deploying and maintaining ML models in production environments using cloud platforms like AWS or GCP
• Solid foundation in software engineering principles, CI/CD pipelines, and microservices architecture
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Experience with LLM orchestration frameworks, vector databases, or large-scale distributed computing systems