Machine Learning Engineer

Machine Learning Engineer (A2) Experience: 2–4 Years Location: Gurugram/ Bangalore

Role Summary

We are looking for a Machine Learning Engineer with 2–4 years of experience to help scale our search and recommendation infrastructure. This role focuses on the end-to end lifecycle of ML products: from building large-scale data pipelines to deploying high availability models in production. You will be responsible for building robust PySpark ETLs, developing PyTorch-based models, and managing Vector Databases to power real-time discovery. While the core applications are traditional search and recommendations, you will also be responsible for fine-tuning LLMs/SLMs for specific use cases. Key Responsibilities • Architect and maintain scalable ETL pipelines using PySpark to process large datasets for feature engineering and model training. • Build and optimize production-grade models using PyTorch. • Implement and optimize Vector Databases for high-dimensional similarity search and retrieval. • Fine-tune LLMs/SLMs for specific search and recommendation tasks, such as semantic query understanding. • Deploy models into production environments as real-time services using inference frameworks like Triton Inference Server, BentoML, or TensorFlow Serving. • Deploy models into production environments as real-time services, ensuring adherence to strict SLAs regarding latency and throughput. • Implement robust monitoring and logging to track model performance, data drift, and system health in a live environment. Technical Requirements • Expert-level proficiency in Python and SQL. • Proven experience with PySpark and distributed computing. • Strong hands-on experience building and optimizing production-grade models using PyTorch. • Practical knowledge of Vector Databases and embedding-based retrieval techniques. • Experience fine-tuning open-source LLMs/SLMs for specialized downstream tasks. • Proficiency with core scientific libraries including NumPy, SciPy, and Matplotlib, Pandas, Scikit-learn, XGBoost/LightGBM, and HuggingFace Transformer • Familiarity with experiment tracking and model versioning tools like MLflow. • Experience with Docker, Kubernetes, and building high-performance APIs. • Professional Qualifications • 2–4 years of experience as an ML Engineer or Data Scientist in a production focused environment. • Deep understanding of the trade-offs between model complexity and real-time inference latency. • Ability to own a project from the data-collection phase through to production deployment and maintenance.