Posted:6 days ago| Platform: Linkedin logo

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Job Type

Full Time

Job Description

Location: Bengaluru (Hybrid)


Role Summary

We’re seeking a skilled Data Scientist with deep expertise in recommender systems to design and deploy scalable personalization solutions. This role blends research, experimentation, and production-level implementation, with a focus on content-based and multi-modal recommendations using deep learning and cloud-native tools.


Responsibilities

  • Research, prototype, and implement recommendation models: two-tower, multi-tower, cross-encoder architectures
  • Utilize text/image embeddings (CLIP, ViT, BERT) for content-based retrieval and matching
  • Conduct semantic similarity analysis and deploy vector-based retrieval systems (FAISS, Qdrant, ScaNN)
  • Perform large-scale data prep and feature engineering with Spark/PySpark and Dataproc
  • Build ML pipelines using Vertex AI, Kubeflow, and orchestration on GKE
  • Evaluate models using recommender metrics (nDCG, Recall@K, HitRate, MAP) and offline frameworks
  • Drive model performance through A/B testing and real-time serving via Cloud Run or Vertex AI
  • Address cold-start challenges with metadata and multi-modal input
  • Collaborate with engineering for CI/CD, monitoring, and embedding lifecycle management
  • Stay current with trends in LLM-powered ranking, hybrid retrieval, and personalization


Required Skills

  • Python proficiency with pandas, polars, numpy, scikit-learn, TensorFlow, PyTorch, transformers
  • Hands-on experience with deep learning frameworks for recommender systems
  • Solid grounding in embedding retrieval strategies and approximate nearest neighbor search
  • GCP-native workflows: Vertex AI, Dataproc, Dataflow, Pub/Sub, Cloud Functions, Cloud Run
  • Strong foundation in semantic search, user modeling, and personalization techniques
  • Familiarity with MLOps best practices—CI/CD, infrastructure automation, monitoring
  • Experience deploying models in production using containerized environments and Kubernetes


Nice to Have

  • Ranking models knowledge: DLRM, XGBoost, LightGBM
  • Multi-modal retrieval experience (text + image + tabular features)
  • Exposure to LLM-powered personalization or hybrid recommendation systems
  • Understanding of real-time model updates and streaming ingestion

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