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 engineering

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