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Mid-level ML role in a Bangalore startup with metro location and common ML requirements increases competition.
ML and LLM skills are transferable across industries, though conversational personalization adds some domain specificity.
Explicit 3–5 year requirement and mandatory LLM/production experience make screening moderately strict.
Own and build production-grade ML/AI systems focused on personalization, long-term user interaction, and conversational AI at scale.
Develop and optimize LLM, RAG, conversational AI, and agentic workflows integrating structured domain knowledge like astrology.
Collaborate cross-functionally to ship impactful AI-driven features that improve user intelligence and engagement.
3-5 years of experience in ML, Applied ML, NLP, or Generative AI engineering.
Proficient in Python programming and strong software engineering fundamentals.
Hands-on experience deploying ML/AI systems to production and working with LLMs, RAG, embeddings, vector search, or conversational AI.
Strong understanding of ML fundamentals, system design, scalability, cloud infrastructure, and evaluation metrics.
Experienced in building end-to-end ML systems with ownership of design, build, evaluation, and production.
Background in personalization or recommendation systems relating to conversational AI or LLMs.
Comfortable working in fast-paced startups focused on AI products that interact with large-scale, longitudinal user data for deep personalization.