General Experience:
SAP Development Landscape Management - Feature-Based Engineering (04/2025 - present):
- End-to-end product design, development of checks in code pipelines, and continuous feature-based enhancements are delivered for the S4 HANA Public Cloud product.
- End-to-end design and development of an orchestration tool to plan, automate, and monitor periodic upgrades of the SAP systems in the entire development landscape of the S4 HANA Public Cloud product.
- Developed a Joule-based AI agent for real-time tracking of code pipelines and associated resource usage from a DevOps perspective.
- Reduced continuous feature enhancements' delivery times by 30%, code pipeline issue resolution times by 40%, and SAP landscape upgrade times by 70%.
- Technologies worked with: Java, Python, SAP BTP, SAP ABAP, Docker, and Git.
SAP Digital Manufacturing Cloud - Central Initial Load, Delta Lake, and Archiving (06/2024 - 03/2025):
- End-to-end product design and development of the archiving process, to replicate manufacturing data from the analytics data store (warm store) to the lakehouse (cold store) in SAP HDLFS, for building AI/ML capabilities upon.
- Technologies worked with: Python, Java Spring Boot, Docker, Kafka, Databricks, Kubernetes, and Git.
SAP Digital Manufacturing Cloud - Insights (06/2023 - 05/2024):
- Full-stack development of new product features.
- Developed product features for real-time analytical views of manufacturing processes that improved the overall process efficiency of customers by 15%.
- Technologies worked with: Java Spring Boot, Python, Docker, Kafka, SAP UI5, Tricentis Tosca, Kubernetes, and Git.
SAP Business Networks – Supply Chain
Collaboration Monitoring (08/2022 - 05/2023):
- Developed intuitive dashboards for a seamless, real-time business process monitoring experience.
- Helped reduce business issue resolution time by 20% via real-time status tracking of buyer/seller transactions.
- Technologies worked with: Grafana, Kapacitor, Telegraf, InfluxDB, Kafka, Docker, Kubernetes, and Git.
SAP Internet of Things (IoT) – Tenant Onboarding (05/2021 - 08/2022):
- Involved in new product feature development.
- Increased regressions were caught during development, and manual test effort was reduced by 10% by increasing automation coverage.
- Technologies worked with: Node.js, Java, Python, Docker, Kubernetes, and Git.
SAP Sales Cloud C4C/CX OData and REST API
Framework (05/2017 - 04/2021):
- Product design, development, and customer incident handling.
- Improved code, productive code, performance by 40%.
- Technologies worked with: SAP ABAP, Node.js, Java, JMeter, Sahi Pro, Docker, Kubernetes, and Git.
AI/ML Experience (January 2020 - present):
- Developed AI/ML solutions as follows.
- Medical reports summarization using LLMs.
- Manufacturing ticket resolution, agentic AI assistant using RAG and LLMs.
- Customer experience improvement through emotion and personality trait detection using deep learning and NLP.
- Automated ticket classification and routing for banking domain using NLP.
- Gesture-based sentiment analysis for sales domain using transfer learning and deep learning.
- Manufacturing defect detection using deep learning.
- Logistics delivery prediction feature using linear regression.
- Static sales forecasting for automobile industry using linear and logistic regression.
- Libraries used: OlmOCR, AWS Textract, OpenAI, Flask, Puppeteer, Langchain, ChatOpenAI, OpenAIEmbeddings, HanaDB, Streamlit, FastAPI, NLTK, Keras, Gensim (FastText, Word2Vec), GloVe, Sklearn, Tensorflow, Sklearn, Pandas, Wordcloud, Spacy, CV2, SKImage, Numpy, Pandas and Statsmodels.
- Technologies used: Python, Node.js, Firebase, DigitalOcean, Databricks, Cloud Foundry, SAP HANA, SQL, Kubernetes, Docker and Git.