Machine Learning Engineer

About The RoleThe role owns the end-to-end lifecycle of production machine learning systems, from designing scalable training pipelines to deploying low-latency models that serve core product features.The engineering team works closely with applied researchers and backend developers to build robust infrastructure where model performance, cost, and reliability are strictly balanced.Key ResponsibilitiesArchitect and implement distributed machine learning pipelines using Python, PyTorch, and Apache Spark for large-scale data processingDeploy, monitor, and scale models in production using cloud infrastructure such as AWS, Docker, and KubernetesOptimize model inference latency, throughput, and memory footprint through quantization, pruning, and ONNX runtimeBuild automated monitoring frameworks to detect feature drift, data quality issues, and performance degradation in real-timeCollaborate with data engineering teams to define feature stores and ensure consistency between training and inference dataWrite clean, highly maintainable code, conduct thorough peer code reviews, and contribute to system architecture documentationWhat We Are Looking For3-6 years of professional software engineering experience, with at least 3 years focused specifically on machine learning engineeringStrong proficiency in Python and deep hands-on experience with production-grade ML frameworks like PyTorch or TensorFlowDemonstrated experience deploying and maintaining containerized ML models in cloud environments such as AWS, GCP, or AzureSolid understanding of software engineering best practices, including CI/CD pipelines, automated testing, and infrastructure-as-codeBS or MS in Computer Science, Machine Learning, Statistics, or a related technical fieldBonus: Experience with LLM fine-tuning, RAG architectures, or contributing to major open-source ML projects