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Mid-level ML role in Bangalore, popular title and metro location increase candidate density.
ML and LLM skills are transferable, but personalization/astrology domain introduces moderate domain bias.
Explicit 3–5 year requirement plus LLM, production and domain skills enforce strict filtering.
Build and own production-grade ML/AI systems end-to-end focused on personalization, memory, recommendations, and user intelligence.
Develop and optimize LLM orchestration, retrieval augmented generation, conversational AI, and agentic workflows with a focus on quality, latency, and cost.
Collaborate with Product and Backend teams to ship impactful features at scale combining structured domain intelligence with ML and LLM reasoning.
3-5 years of experience in Machine Learning, Applied ML, NLP, or Generative AI engineering.
Strong Python programming and software engineering fundamentals.
Hands-on experience with LLMs, RAG, embeddings, vector search, or conversational AI and production deployment of ML/AI systems.
Strong understanding of ML fundamentals, system design, scalability, APIs, evaluation, and cloud platforms.
Experience working at the intersection of AI personalization and human relationship modeling in consumer-facing products.
Proven ability to own end-to-end ML system design, build, evaluation, and production deployment with operational responsibility.
Technical depth in large language models, retrieval techniques, and conversational AI within rapidly scaling B2C environments.