Machine Learning Engineer

We are seeking an accomplished Machine Learning Engineer to enhance our search and recommendation infrastructure. This pivotal role encompasses the complete lifecycle of machine learning products, from developing scalable data pipelines to deploying highly available models in production environments. The successful candidate will be instrumental in creating robust PySpark ETLs, developing advanced PyTorch-based models, and managing Vector Databases for real-time discovery. While the primary focus is on traditional search and recommendation systems, this role also involves the fine-tuning of LLMs/SLMs for specific applications, such as semantic query understanding. Key responsibilities include architecting and maintaining scalable ETL pipelines for large datasets, building and optimizing production-grade PyTorch models, implementing and refining Vector Databases for similarity search, and deploying models as real-time services using inference frameworks like Triton Inference Server or BentoML. Ensuring strict SLAs for latency and throughput, and implementing comprehensive monitoring and logging for model performance and system health are also critical. Essential technical proficiencies include expert-level Python and SQL, proven experience with PySpark and distributed computing, hands-on PyTorch model development, practical knowledge of Vector Databases and embedding techniques, experience fine-tuning LLMs/SLMs, familiarity with scientific libraries (NumPy, Pandas, Scikit-learn, HuggingFace Transformers), experiment tracking tools (MLflow), and containerization technologies (Docker, Kubernetes).