VARSHITH REDDY RAAVI
Professional Summary
Data Scientist with 3+ years of experience delivering ML, MLOps and generative AI solutions across research, e-commerce, healthcare-adjacent and insurance-adjacent domains. Experienced in model development, fine-tuning LLMs, building scalable inference pipelines, data engineering on BigQuery, and deploying reproducible ML workflows on cloud and Kubernetes. Skilled at translating business problems into production-ready ML systems with attention to evaluation, explainability, and safety.
Technical Skills
Work Experience
- Designed and implemented an end-to-end AI music generation platform combining lyric generation, sentiment analysis, and audio synthesis to enable interactive song creation for research demos.
- Fine-tuned GPT-2 on the GeniusLyrics dataset to produce context-aware, genre-specific lyrics; achieved ~92% measured alignment between generated lyrics and target emotion labels.
- Integrated Bark and MusicGen for phoneme-aware vocal synthesis and optimized the audio pipeline to reduce end-to-end inference latency by 30%, improving interactivity for user sessions.
- Built scalable inference pipelines for lyric generation, sentiment classification, and audio rendering; containerized services for reproducible experiments and faster iteration.
- Instrumented evaluation pipelines using automatic metrics and human-in-the-loop evaluations to validate emotional alignment and generation quality across genres.
- Authored reproducible training and deployment notebooks using Hugging Face Transformers and PyTorch; standardized preprocessing and checkpointing to accelerate model iteration.
- Engineered an adversarial prompt-generation framework combining reinforcement learning (PPO) and evolutionary search to discover prompts that bypass model safeguards.
- Executed controlled evaluations of prompt attacks against GPT-4, Claude, and LLaMA families, reporting an attack success rate exceeding 80% in sandbox experiments to inform mitigation research.
- Designed a multi-objective benchmark measuring attack success, stealthiness, and reproducibility to systematically compare attack strategies and model vulnerabilities.
- Developed tooling to automate large-scale prompt generation, scoring, and logging for reproducible MLSecOps experiments and ablation studies.
- Proposed and validated mitigation strategies (prompt sanitization heuristics and ranking-based filtration) that reduced successful exploit prompts in lab tests.
- Documented experimental protocols, safety considerations, and reproducible artifacts to support follow-on research and responsible disclosure of vulnerabilities.
- Analyzed 10M+ retail transactions to identify sales trends, regional demand patterns, and promotion impact; insights guided merchandising and pricing decisions.
- Built scalable ingestion and transformation pipelines handling 15M+ daily records from sales, inventory, and pricing systems to feed analytics and ML models.
- Developed optimized analytical queries and data models in BigQuery to compute KPIs (revenue, basket size, sell-through), cutting report generation time by ~30%.
- Designed and produced interactive dashboards and operational reports in Looker Studio tracking performance across 1,000+ retail locations for commercial stakeholders.
- Performed statistical analysis and A/B style attribution to quantify seasonality and promotion lift; recommendations led to prioritized SKU assortment changes.
- Optimized ETL workflows with Python and SQL to reduce pipeline latency and improve data freshness for downstream ML and business reporting.
Education
Certifications
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