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Suloni Praveen
Entry-Level Software Engineer specializing in data engineering and ML systems
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About
Built an end-to-end Next.js/TypeScript LLM-based scientific PDF analyzer using local Ollama/Llama inference to prioritize privacy and cost, producing structured research artifacts (e.g., authors/methods/findings) with ~92% extraction accuracy. At Qualtrics, helped replace a batch pipeline with a real-time, low-latency ML inference service (Python/Go on Kubernetes) using Redis caching, Grafana-based observability, and graceful fallbacks to protect UX during failures.
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Key Strengths
- Owned end-to-end full-stack TypeScript/Next.js LLM document analysis system (architecture through deployment)
- Schema-based validation to make LLM outputs reliable and UI-safe
- Designed low-latency real-time ML inference services with caching (Redis) and strict API boundary validation
- Production reliability patterns: exponential backoff retries, request-level idempotency, graceful fallbacks
- Strong observability practice using logging + Grafana dashboards to drive API/contract improvements
- Model selection and thresholding using metrics (F1, confidence scores) to reduce user-facing false prompts
- Quantified extraction performance (~92% accuracy) on evaluation set
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Experience
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Publications
1 publication
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