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 #J-18808-Ljbffr