S

Sayali Deshmukh

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Gujarat, India

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work
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Résumé

Jobs verified_user 0% verified
  • S
    AI/ML Engineer
    Snowflake,
    Nov 2023 - Current (2 years 8 months)
    • Contributed to development and deployment of ML models (classification, NLP, recommender systems) in Snowpark ML, improving in-database inference efficiency by 32% and reducing reliance on external data pipelines. • Developed feature engineering workflows in Snowflake Data Cloud using SQL, Python, and Snowpark APIs, enabling the processing of 50+ TB of structured/unstructured data daily for ML use cases. • Assisted in building retrieval-augmented generation (RAG) pipelines with Cortex AI embeddings and vector search, helping reduce hallucination rates by 27% in enterprise-facing AI applications. • Implemented components of ETL → training → serving pipelines with Airflow and dbt, improving reproducibility and reducing pipeline latency
  • I
    AI/ML Engineer
    IBM,
    Jan 2020 - Mar 2022 (2 years 3 months)
    • Developed and deployed scalable ML models for classification, forecasting, and anomaly detection using scikit-learn, XGBoost, and LightGBM, improving operational risk scoring accuracy by 31% across financial services and ERP clients (SAP, NetSuite). • Designed enterprise-grade NLP systems using spaCy, BERT, NLTK, and Hugging Face Transformers for named entity recognition (NER), text classification, and document parsing to process unstructured reports, contracts, and user feedback. • Built multilingual sentiment analysis models integrated into customer service platforms, enhancing automation and customer satisfaction tracking across English, Hindi, and Marathi. • Engineered ETL pipelines using Apache Spark, PySpark, Presto, and Hive, i
Education verified_user 0% verified
  • D
    MSc In
    Deogiri college
  • Northeastern University
    Masters Of Science in
    Northeastern University
  • S
    Bsc
    Shri Shivaji College of Agri Biotechnology
Projects (professional or personal) verified_user 0% verified
  • R
    Responsible AI Monitoring & Governance Platform
  • R
    Real-Time Recommendation Engine with Snowpark ML
    • Developed a real-time recommendation system leveraging Snowpark ML and PyTorch, processing 50+ TB of structured/unstructured data daily from multi-cloud sources (AWS S3, Azure Data Lake, GCP BigQuery). • Served personalized recommendations to 300+ enterprise users through FastAPI + TorchServe microservices. • Optimized models with ONNX runtime and INT8 quantization, reducing inference costs by 35% while maintaining accuracy. • Automated feature engineering pipelines with Airflow + dbt, cutting batch latency by 40%. • Integrated Grafana dashboards for real-time monitoring of engagement metrics, improving system adoption by product teams. Built a governance and monitoring platform for deployed ML models using MLflow, Prometheus, and Gr
  • E
    Enterprise RAG Pipeline with Snowflake Cortex AI
    • Designed and deployed a retrieval-augmented generation (RAG) pipeline using Snowflake Cortex AI embeddings + vector search to improve enterprise chatbot and document summarization accuracy. • Fine-tuned LLaMA-2/3 models on enterprise knowledge bases with Hugging Face Transformers, reducing hallucination rates by 27%. • Built hybrid search with BM25 + dense embeddings, improving factual correctness in responses. • Deployed pipeline using Kubernetes, Docker, and MLflow, achieving <100ms inference latency at scale. • Implemented data governance controls with role-based access, ensuring GDPR/KYC compliance for enterprise data usage.