Machine Learning Engineer

About The RoleThe role focuses on building, scaling, and maintaining the core machine learning infrastructure and production models that power real-time personalization and predictive features. This position sits at the intersection of data engineering and applied ML, translating research-grade algorithms into robust, low-latency production services.The engineer will collaborate closely with product and data platform teams to design end-to-end ML pipelines, optimize training and inference performance, and establish rigorous evaluation and monitoring patterns across the entire model lifecycle.Key ResponsibilitiesDesign, build, and deploy production-grade machine learning models for classification, recommendation, and NLP use cases.Develop and maintain scalable data pipelines and feature stores using Python, SQL, and PySpark to support offline training and online serving.Optimize model inference latency and throughput for real-time APIs using Triton Inference Server, ONNX, or TensorRT.Implement automated CI/CD pipelines for ML (MLOps) using tools like MLflow, Kubeflow, or AWS SageMaker to automate model training, testing, and deployment.Set up continuous monitoring and alerting for data drift, concept drift, and model performance degradation in production environments.Write clean, modular, and well-tested code in Python or Go, participating in code reviews and architecture design sessions.What We Are Looking For3+ years of professional software engineering or machine learning engineering experience, with a proven track record of deploying models to production.Strong programming skills in Python and deep familiarity with ML frameworks such as PyTorch, TensorFlow, or XGBoost.Hands-on experience with cloud infrastructure (AWS or GCP) and containerization technologies like Docker and Kubernetes.Solid understanding of software engineering best practices, including version control, CI/CD, unit testing, and system design.BS or MS in Computer Science, Data Science, Mathematics, or a related quantitative field.Bonus: Experience with vector databases (Pinecone, Milvus), Triton, or managing large-scale distributed training workloads.