Anand Krishna

Designed and implemented a production-grade multi-agent summarization framework for automating multi-step reasoning across clinical documents, accelerating review workflows by 45%. Deployed RAG ingestion pipelines on AWS S3 and integrated Human-in-the-Loop (HITL) workflows for data validation and regulatory compliance.

Developed machine learning endpoints using classification techniques (random forest, XGBoost) to improve anomaly detection rates by 28%. Built automated Python data pipelines to clean, normalize, and extract diagnostic features, saving 15 hours per week in manual preparation.

Developed end-to-end Python automation solutions with statistical risk models, reducing operational auditing lifecycles from days to hours. Conducted testing for medical devices to ensure compliance and developed/executed test plans for software applications in surgical environments.

Engineered Python-based text extraction scripts to accelerate gap assessments and DHF remediation during a transition from MDD to EU-MDR. Standardized FMEA records using programmatic data cleansing and automated extraction/structuring of risk assessment and RACT mitigation data.

Reviewed and modified GD&T drawings and prepared PPAP quality documents for component submission to ensure industry compliance. Conducted feasibility studies and collaborated with cross-functional teams to design manufacturing/process solutions.

Prepared PPAP documents including Control Plan, FMEA, PFD, and SDS, and reviewed drawings to support process study and layout inspection for project timelines. Performed documentation and drawing reviews to support manufacturing/process planning.

M.Tech in Artificial Intelligence and enterprise AI model serving. Bridged experimental AI models with production-ready enterprise systems using FastAPI, Docker, Kubernetes, and MLflow.

Grade: 3

Activities and societies: Part of Organising Committee for any kind fest at the college

Graduated in Mechanical Engineering with specializing New Product Development.