Aishwarya Patil
Professional Summary
Gen AI Engineer with 5 years of experience designing, developing, and deploying enterprise Generative AI and ML solutions using Azure OpenAI, LangChain, Python, and production MLOps practices. Experienced in RAG architectures, vector databases, transformer-based NLP, AI agents, and cloud-native deployments that improved model deployment efficiency, search accuracy, and document processing throughput across healthcare, financial services, and e-commerce domains.
Technical Skills
Work Experience
- Led design and implementation of Retrieval-Augmented Generation solutions using Azure OpenAI, LangChain, and embeddings to index enterprise knowledge, improving knowledge discovery efficiency by 41%.
- Engineered conversational AI platforms and AI agents built on LLMs with prompt engineering and LangChain orchestration, reducing manual query handling by 33% through automated intent resolution.
- Designed and deployed semantic search architectures using vector indexing and Azure AI Services to enable contextual document retrieval and enterprise search with measurable accuracy gains.
- Built AI-powered document intelligence pipelines combining transformer models, OCR, and NLP to automate contract and compliance analysis, accelerating document processing workflows and cutting manual review time.
- Developed production FastAPI microservices for model inference and secure AI integration on Azure, ensuring high availability, scale, and enterprise security controls for LLM consumption.
- Automated model evaluation and monitoring with MLflow and observability tooling to validate production LLM outputs, improving deployment confidence and governance across models.
- Developed scalable machine learning models and training pipelines with Python and TensorFlow on cloud platforms, improving financial forecasting accuracy by 27%.
- Implemented automated feature engineering and distributed data processing workflows using PySpark and Databricks, improving ML development throughput by 29%.
- Designed end-to-end MLOps workflows with MLflow, Docker, and GitHub Actions to enable repeatable CI/CD model deployments and environment promotion across staging and production.
- Built secure, high-performance model inference APIs and microservices to serve ML models with monitoring and rollback capabilities, supporting enterprise AI consumption.
- Optimized dataset preprocessing, validation, and quality checks to reduce data drift and improve model reliability across production pipelines.
- Delivered NLP solutions for sentiment analysis and information extraction to support investment analytics and business intelligence initiatives.
- Built predictive analytics models for customer behavior and demand forecasting using Python and Scikit-learn, improving forecast accuracy by 23%.
- Implemented end-to-end ML pipelines for segmentation and recommendation using TensorFlow and feature engineering, improving targeted marketing outcomes.
- Conducted exploratory data analysis with SQL and Python to identify trends and provide actionable insights that informed product and marketing strategies.
- Automated analytical reporting and integrated model outputs into dashboards, reducing manual reporting effort by 21% and improving stakeholder visibility.
- Applied NLP techniques for customer feedback analysis and sentiment classification to surface product issues and feature requests for product teams.
- Collaborated with product and experimentation teams to deploy A/B testing frameworks and ensure reproducible analytics across large-scale datasets.
Education
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