Machine Learning Engineer

About The Role

The role focuses on building, scaling, and maintaining the core machine learning models and pipelines that power real-time decision-making systems. This engineer will translate applied research into high-throughput production services, ensuring models meet strict performance, latency, and reliability requirements.

The team works at the intersection of data engineering and applied ML, collaborating closely with backend engineers and product owners. This role is critical to scaling model deployment infrastructure, automating training workflows, and establishing robust MLOps practices across the organization.

Key Responsibilities

• Design and deploy production-grade machine learning models for real-time inference, focusing on recommendation systems, classification, and predictive modeling
• Build and optimize scalable data preprocessing and feature engineering pipelines using Apache Spark, Ray, or PySpark
• Develop and maintain automated CI/CD pipelines for model training, evaluation, and deployment using tools like Kubeflow, MLflow, or Argo Workflows
• Implement real-time monitoring and alerting for model drift, concept drift, and system latency in production environments
• Optimize model inference performance through quantization, pruning, and GPU acceleration techniques using TensorRT or ONNX
• Collaborate with platform engineers to design and scale vector databases and feature stores for low-latency retrieval

What We Are Looking For

• 3–6 years of professional experience as a Machine Learning Engineer or Software Engineer working with production-grade ML systems
• Strong proficiency in Python and hands-on experience with core ML frameworks such as PyTorch, TensorFlow, or XGBoost
• Proven experience deploying models to cloud environments (AWS, GCP, or Azure) using containerization tools like Docker and Kubernetes
• Solid understanding of software engineering best practices, including version control, unit testing, and design patterns
• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field
• Bonus: Experience with Triton Inference Server, Triton CLI, or building custom Triton backends for high-throughput serving