Machine Learning Engineer

About the role

About The Role

The role owns the end-to-end development and productionization of scalable machine learning systems, bridging the gap between exploratory data science and robust software engineering.

The engineering team builds high-throughput inference services, optimized feature stores, and automated training pipelines to support advanced AI capabilities at scale.

Key Responsibilities

Architect, train, and deploy high-performance machine learning models for production environments using Python and PyTorch

Build and maintain low-latency inference endpoints and scalable feature engineering pipelines utilizing Apache Spark and Docker

Implement comprehensive monitoring systems to track model drift, prediction latency, and system resource utilization

Optimize model architectures for throughput and cost efficiency through quantization, pruning, and distributed training techniques

Collaborate with backend engineers to integrate ML services into existing microservices architecture via robust REST and gRPC APIs

Conduct thorough code reviews, write automated unit and integration tests, and contribute to engineering documentation

What We Are Looking For

3 to 6 years of professional experience in machine learning engineering or applied software engineering, with multiple models successfully deployed to production

Advanced proficiency in Python and deep working knowledge of core ML frameworks such as PyTorch or TensorFlow

Demonstrated hands-on experience with cloud infrastructure and MLOps tools including AWS, GCP, Docker, Kubernetes, and MLflow

Solid foundation in software design patterns, data structures, algorithms, and distributed computing principles

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Statistics, or a related technical field

Bonus: Experience fine-tuning large language models, contributing to open-source AI projects, or holding an active cloud certification