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Priyanka Kaswan
Senior AI Research Engineer specializing in LLM agents and large-scale ML
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About
AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.
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Key Strengths
- Deployed production LLM system generating 200–400 line Snowflake SQL for network failure/anomaly analysis
- Built multi-agent architecture with isolated roles and structured JSON interfaces
- Strong reliability focus: schema validation, presence-map checks, step-by-step plans, and guardrails to prevent SQL hallucinations
- Data scale experience: anomaly detection over ~11B event records added daily
- Measurement-driven iteration: golden test suite, live monitoring (SQL success rate/latency/precision-recall), and turning failures into new test cases
- Effective collaboration with non-technical RAN engineers via rapid prototyping and feedback-driven iteration
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5 publications
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