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Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
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About
Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.
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Key Strengths
- Built and deployed production LLM hallucination detection/monitoring using semantic entropy
- Designed scalable async processing architecture with Redis queues and Celery workers
- Strong observability practices (structured logging + PostgreSQL analytics/monitoring)
- Pragmatic evaluation strategy for ambiguous labels (controlled prompts + human review)
- Reliability-focused AI workflow design balancing correctness, latency, and cost
- Effective collaboration with non-technical stakeholders using examples, dashboards, and threshold alignment
Reference Highlights
- Wrote strong foundational code
- Foundational contributions to algorithm components
- Valuable contributor on a small team
- Collaborative in design discussions
- Adaptable across multiple parts of the stack
- Bridges technical and non-technical communication effectively
- Improved communication over time
- Able to focus messaging on high-impact details for stakeholders
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Experience
Education
Awards
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Publications
1 publication
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