Machine Learning Engineer

About The Role The role focuses on the end-to-end design, implementation, and scaling of machine learning models that power core product features and real-time decision engines. The engineer will build resilient data pipelines, train high-performing models, and deploy them to production environments where sub-millisecond latency and high reliability are critical. Working within a cross-functional team of data scientists and platform engineers, this role bridges the gap between experimental prototyping and robust production engineering. The team prioritizes clean system design, automation of training workflows, and the continuous monitoring of model performance. Key Responsibilities Design, train, and optimize machine learning models utilizing PyTorch, TensorFlow, or XGBoost to solve complex tabular and NLP problems. Deploy production-grade ML pipelines and APIs on AWS using SageMaker, Kubernetes, and Docker. Build and maintain scalable feature stores and data pipelines using PySpark, SQL, and DBT to feed training and inference systems. Implement automated monitoring and alerting systems to detect model drift, concept drift, and performance degradation in real-time. Collaborate with backend engineers to integrate model endpoints into microservices architecture, ensuring low-latency inference. Establish MLOps best practices including CI/CD for ML, model versioning with MLflow or DVC, and automated retraining. What We Are Looking For 3-6 years of experience as a Machine Learning Engineer or Software Engineer with a heavy focus on production ML systems. Strong proficiency in Python and solid software engineering fundamentals, including dry code, unit testing, and system design. Hands-on experience with cloud infrastructure (AWS or GCP) and containerization technologies like Docker and Kubernetes. Deep understanding of ML algorithms, model evaluation techniques, optimization, and statistical modeling. BS, MS, or PhD in Computer Science, Data Science, Mathematics, or a related quantitative field. Bonus: Experience with real-time streaming technologies like Kafka, or building GenAI/LLM pipelines using LangChain. Show more Show less