Sep 2024 – Jun 2025 · 9 mos · Pune, Maharashtra, India · Hybrid

• Delivered enterprise-scale predictive analytics solutions that drove strategic healthcare decisions through advanced machine learning and AI technologies.
• Key Achievements:
• Advanced Analytics Development: Built and deployed comprehensive predictive models using Apache Spark, PySpark, and Python, integrating deep learning frameworks (TensorFlow, PyTorch) with traditional machine learning (scikit-learn) to enable accurate forecasting and real-time inference for healthcare clients.
• Data Pipeline Architecture: Designed and implemented end-to-end scalable data processing systems that aggregate and transform large-scale Veeva Pulse and claims data. These pipelines automated model training, tuning, and deployment workflows while ensuring reliable pre- and post-quarter data extrapolation for critical business insights.
• Machine Learning Infrastructure: Developed robust data preprocessing frameworks featuring automated train-test splits, intelligent missing value imputation, and advanced feature encoding techniques. These innovations significantly improved predictive model accuracy for patient classification and segmentation initiatives.
• Generative AI Innovation: Contributed to cutting-edge generative AI solutions using FastAPI, OpenAI APIs, and Hugging Face Transformers, successfully transforming complex clinical narratives into clear, actionable business intelligence for healthcare stakeholders.
• Technical Leadership: Collaborated effectively with cross-functional teams in a fast-paced environment, continuously expanding expertise in emerging AI technologies while delivering high-impact solutions that met evolving business requirements.
• Technologies: Python, PySpark, Apache Spark, TensorFlow, PyTorch, scikit-learn, FastAPI, OpenAI API, Hugging Face Transformers, Veeva Pulse, SQL

Apr 2023 – Sep 2024 · 1 yr 5 mos · Pune, Maharashtra, India · Hybrid

• Engineered a cloud-native generative AI solution in Python on AWS for financial crime analytics, automating text summarization to enhance alert evaluation in fraud detection and AML investigations.
• Implemented scalable AWS Lambda handlers, custom Lambda layers, and AWS Bedrock integrations—fully automated via Terraform—to ensure reliable real-time transaction monitoring and risk management.
• Fine-tuned AI models using TensorFlow, PyTorch, and Hugging Face Transformers to generate actionable narratives, significantly improving decision-making efficiency and compliance reporting.
• Developed secure RESTful APIs using AWS API Gateway to integrate financial data streams, coupled with robust GitLab and Jenkins CI/CD pipelines for streamlined deployment and regulatory compliance.

Jul 2022 – Mar 2023 · 8 mos · Hyderabad, Telangana, India · On-site

• Improved driver testing processes by building machine learning models that prioritize test cases and automatically classify failures, reducing overall testing time by about 30%.
• Used scikit-learn and PySpark to create a K-means clustering model for grouping test cases, and applied Random Forest classification to pinpoint issues quickly.
• Shifted descriptive analysis from an LSTM-based model to a BERT-based approach for generating clearer, more accurate issue descriptions.
• Applied Low-Rank Adaptation (LoRA) techniques with PyTorch to fine-tune model performance and developed a CNN-based solution for real-time audio anomaly detection.
• Combined full-stack methods with data science by setting up end-to-end pipelines for data collection, preprocessing, and model integration.

Aug 2021 – Oct 2021 · 2 mos · India

• Participated in designing and Developing App screens and their workflow using Activity and Fragments.
• Written application logic using Android SDK and Android Studio.
• Developed rich UI for the applications modules using ListView, Scroll View, View Pager Navigation Drawer and
• Developed Custom View.