Posted:3 days ago| Platform: Indeed logo

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On-site

Job Type

Full Time

Job Description

Key Responsibilities:

  • Build, train, and validate machine learning models for prediction, classification, and clustering to support NBA use cases.
  • Conduct exploratory data analysis (EDA) on both structured and unstructured data to extract actionable insights and identify behavioral drivers.
  • Design and deploy A/B testing frameworks and build pipelines for model evaluation and continuous monitoring.
  • Develop vectorization and embedding pipelines using models like Word2Vec, BERT, to enable semantic understanding and similarity search.
  • Implement Retrieval-Augmented Generation (RAG) workflows to enrich recommendations by integrating internal and external knowledge bases.
  • Collaborate with cross-functional teams (engineering, product, marketing) to deliver data-driven Next Best Action strategies.
  • Present findings and recommendations clearly to technical and non-technical stakeholders.

Required Skills & Experience:

  • Strong programming skills in Python, including libraries like pandas, NumPy, and scikit-learn.
  • Practical experience with text vectorization and embedding generation (Word2Vec, BERT, SBERT, etc.).
  • Proficiency in Prompt Engineering and hands-on experience in building RAG pipelines using LangChain, Haystack, or custom frameworks.
  • Familiarity with vector databases (e.g., PostgreSQL with pgvector, FAISS, Pinecone, Weaviate).
  • Expertise in Natural Language Processing (NLP) tasks such as NER, text classification, and topic modeling.
  • Sound understanding of supervised learning, recommendation systems, and classification algorithms.
  • Exposure to cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes) is a plus.

Experience – 5+ years

Job Type: Full-time

Pay: ₹800,000.38 - ₹3,000,000.60 per year

Benefits:

  • Health insurance
  • Paid time off

Schedule:

  • Day shift

Location:

  • Viman Nagar, Pune, Maharashtra (Preferred)

Work Location: In person

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