Improved CI/CD infrastructure by refactoring GitHub Actions workflows, reducing build time by 30% and improving deployment reliability.

Developed automated database migration pipelines using Flyway and PostgreSQL to maintain schema consistency across distributed environments.

Integrated machine learning services by implementing OpenAI Chat Completion APIs for sentiment analysis and automated content moderation.

Deployed AI-driven spam detection systems using Azure AI models, reducing inappropriate content by 25% with feature-flag based rollout strategies.

Collaborated with cross-functional teams to design, test, deploy, and monitor software services in cloud environments.

Trained probes on ∼ 250k-sample, six-benchmark VQA; late transformer layers achieved AUROC ∼ 0.93 on Gemma-3, Llama, LLaVA, Qwen.

Built a 3 Agent workflow to synthesize image–text pairs: VLM-guided image generator → QA generator → validator, with batch orchestration.

Proposed a novel hybrid NER architecture to address annotation inaccuracies and generative hallucinations in biomedical entity recognition.

Developed a multi-stage fusion architecture combining BERT-based classifier with RAG LLM as a judge for entity-level factual grounding.

Developed modular UI components and backend services that automated operational workflows, reducing manual workload from 40 hours/week to minutes.

Built analytics dashboards using Chart.js and user behavioral data to generate insights that increased revenue by 30%.

Implemented automation pipelines and scheduled scripts for referral code generation, increasing new user signups by 23%.

Collaborated with engineering teams to deploy scalable web services on AWS EC2 and Firebase cloud infrastructure.

Trained and evaluated deep learning models achieving 94% prediction accuracy.

Implemented model training pipelines using TensorFlow and Python for scalable ML experimentation.