Senior ML Engineer

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟱𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟴𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟱-𝟴𝟱 𝗟𝗣𝗔) Experience: 4+ yrs Location: NCR, Delhi, India As a Senior ML Engineer, you will lead the design and deployment of cutting‑edge recommendation and personalization solutions that drive user engagement across our digital ecosystems. You will collaborate closely with data scientists, product managers, and engineering teams to build high‑throughput, low‑latency recommendation engines that adapt in real time to user behavior and contextual signals. By leveraging graph-based match‑scoring, sophisticated ranking models, and innovative cold‑start strategies, you will enhance the relevance of content and product suggestions for millions of users worldwide. Your work will directly influence key metrics such as click‑through rate, conversion rate, and user retention, while ensuring scalability, robustness, and maintainability of the underlying machine learning infrastructure. Requirements 𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 • Design, develop, and production‑grade real‑time recommendation pipelines that process millions of events per day. • Engineer graph‑based match‑scoring algorithms to uncover latent user‑item relationships and improve match quality. • Build and fine‑tune ranking models using learning‑to‑rank, gradient‑boosted trees, or deep neural network approaches. • Develop cold‑start solutions for new users and items using hybrid collaborative filtering, content embeddings, and transfer learning. • Perform feature engineering at scale, ensuring feature freshness, consistency, and low latency latency requirements. • Collaborate with data engineering teams to optimize data ingestion, storage, and retrieval for recommendation workloads. • Conduct rigorous A/B testing, statistical analysis, and interpret model outputs to drive data‑driven decisions. • Mentor junior ML engineers, sharing best practices, code review, and debugging techniques. • Stay abreast of industry research, publish findings, and iterate on emerging techniques such as reinforcement learning or multi‑armed bandit frameworks. • Ensure robust monitoring, logging, and alerting for model drift, latency, and error rates across all services. 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗬𝗼𝘂 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 • 4+ years of hands‑on experience building large‑scale recommendation systems in a production environment. • Deep expertise in personalization, ranking, and real‑time recommender architectures. • Strong background in graph theory, graph neural networks, and bipartite graph matching for recommendation tasks. • Proven track record of addressing cold‑start challenges with innovative techniques and measurable impact. • Proficiency in Python, SQL, Spark/Databricks, and a major cloud platform (AWS, GCP, Azure). • Solid experience with ML frameworks such as TensorFlow, PyTorch, LightGBM, and XGBoost. • Demonstrated ability to translate metrics and business objectives into actionable ML solutions. • Excellent communication skills, capable of presenting complex technical concepts to non‑technical stakeholders. • A collaborative mindset, comfortable working in cross‑functional teams in a fast‑paced, iterative environment. • Passion for staying current with academic research and industry trends, and willingness to experiment with new ideas.