Machine Learning Engineer

About The Role The role owns the full lifecycle of machine learning systems, from experimental research and prototyping to reliable, low-latency production deployments. The engineering team builds robust ML infrastructure and production-grade models that scale to support core business operations and millions of daily queries. Key Responsibilities Design, train, and validate machine learning models using PyTorch and Python for complex production use cases Build high-throughput data ingestion and feature engineering pipelines using Apache Spark and distributed computing frameworks Deploy, monitor, and scale models on AWS using SageMaker, Docker, and Kubernetes with strict latency and reliability guarantees Implement comprehensive monitoring systems to track data drift, concept drift, and prediction latency in production Collaborate with backend engineers to integrate model endpoints into scalable microservices and APIs Conduct code reviews, write unit and integration tests, and establish best practices for machine learning What We Are Looking For 3-6 years of professional experience in software engineering, with at least 3 years focused on machine learning engineering in production Expert proficiency in Python and deep familiarity with PyTorch or TensorFlow Hands-on experience with cloud infrastructure and MLOps tools such as AWS SageMaker, MLflow, Docker, and Kubernetes Solid understanding of distributed systems, data structures, and algorithms BS or MS in Computer Science, Machine Learning, or a related quantitative field Bonus: Experience with large language models, vector databases, or real-time streaming architectures using Kafka #J-18808-Ljbffr Evlo AI