Senior ML Engineer

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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

• 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.
• 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.