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Yukta Kulkarni
Junior AI/ML Engineer specializing in applied LLMs, security, and reinforcement learning
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About
Built and shipped a production LLM-powered investor research feature for a fintech product, focused on grounded answers and minimizing hallucinations. Implemented retrieval-quality and evidence-coverage gating with clear refusal fallbacks, and evaluates systems with regression tests and metrics like correct-refusal rate, hallucination rate, and latency. Comfortable orchestrating workflows with LangChain or custom Python depending on production needs.
Experience
Education
Key Strengths
- Built and deployed a production LLM-powered investor research tool used by real users
- Designed reliability guardrails: retrieval-quality confidence checks, evidence coverage gating, and safe refusals
- Systematic evaluation approach using real/edge-case queries and regression tests
- Metrics-driven iteration (accuracy vs correct refusals, hallucination rate, latency)
- Pragmatic orchestration choices (LangChain for rapid iteration vs custom Python for production control/latency/debuggability)
- Strong ability to translate product expectations into technical decisions with non-technical stakeholders
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