About#

Experience#

• Kognitic AI — Machine Learning Engineer (Jun 2024 – Present)
  Owned end-to-end development and productionization of a $1M-revenue AI SaaS platform for clinical intelligence; deployed natural language analytics with MCP-based trust layers and multimodal LLM agents; built product analytics dashboards; developed Transformer models for biomarker and drug-target extraction (87% accuracy; EMNLP 2025/ACL); fine-tuned LLMs with LoRA + RLHF for clinical eligibility prediction.

• Kognitic AI — Machine Learning Intern (June 2023 – Sept 2023)
  Built scalable ETL pipelines with AWS Glue (Spark), S3, Athena; implemented time-series forecasting and automated CI/CD pipelines; developed time-series forecasting models (XGBoost ensembles); automated reporting on AWS; engineered ETL pipelines at 10M+ record scale.

Education#

• MS in Data Science, UC San Diego (Aug 2022 – Jun 2024)
  Courses: NLP, Data Engineering, Statistics, Deep Learning, HPC.
  Award: UCSD Excellence in Academic Achievement.

Publications#

• BIOPSY — Biomarkers In Oncology: Pipeline for Structured Yielding (ACL / EMNLP 2025)
• Closed Domain Multiple Choice Question Answering (Springer Journal 2023)
• Pneumonia Detection using an Ensemble of Modified ViT-YOLO Models (IEEE Conference 2022)

Skills#

Python, SQL, C++, R; PyTorch, TensorFlow, JAX; LangGraph, LangChain; MLflow, Grafana; PySpark, Hadoop; MongoDB, PostgreSQL, Neo4j, Pinecone, Snowflake, Redshift, dbt; Airflow; AWS (Lambda, S3, CloudWatch, Athena, EC2); GCP; GitHub Actions, Jenkins.

Contact#

• Email: schetwani1@gmail.com
• LinkedIn: https://www.linkedin.com/in/sanyachetwani
• GitHub: https://github.com/sanyacodes