Srujith Bollam
Professional Summary
Generative AI / NLP Engineer with 0 years of experience building LLM-based applications and NLP models. Hands-on experience designing and deploying Transformer-based agents, curating large text corpora, and creating source-grounded retrieval workflows using Hugging Face and vector retrieval systems.
Technical Skills
Work Experience
- Built 3+ AI agents using Hugging Face Transformers, Datasets, and Inference APIs to prototype core NLP workflows and accelerate iteration on model-based features.
- Collected, cleaned, and maintained a large-scale text corpus (de-duplication, normalization, metadata), and published documentation and code at code.swecha.org/srujithbollam for reproducibility.
- Developed and fine-tuned sentiment analysis Transformer models, training and evaluating on benchmark datasets to achieve 95% accuracy in experimental evaluations.
- Implemented evaluation and benchmarking pipelines (train/validation/test splits and metric tracking) using Hugging Face Datasets to ensure reproducible model comparisons.
- Integrated models with inference endpoints via Hugging Face Inference API and documented API contracts and deployment steps to enable engineering reuse.
- Collaborated using Git-based workflows, authored technical experiment reports, and maintained well-documented code to support handoffs in an engineering environment.
Projects
- Implemented sport and difficulty selection with quizzes delivered in batches of 4–5 questions and a quiz-regeneration feature for improved user flow.
- Integrated ChromaDB retrieval over a curated sports knowledge corpus to provide source-grounded answers and attribution for each generated question.
- Added optional DuckDuckGo web search to fetch up-to-date sports information when needed, improving factuality of generated content.
- Implemented optional OpenAI-powered question generation constrained to a strict JSON schema and included retrieved sources for each generated question.
- Designed a deterministic, source-grounded fallback system enabling full dashboard functionality without external API keys for offline operation.
- Built a GenAI application that converts natural language queries into optimized SQL statements using an LLM API, achieving ~98% query accuracy on a structured test suite.
- Designed input validation logic to detect malformed and ambiguous natural language queries before SQL generation, reducing incorrect query execution.
- Applied iterative prompt engineering and targeted test cases to improve SQL generation accuracy and reliability across edge-case queries.
- Implemented validation and execution safeguards for generated SQL and documented system design, testing procedures, and implementation details.
Certifications
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