Adithya K Ramesh

AI/ML Experience:

Medical Reports Summarization (01/2025 to present):

• Designed microservices to generate medical summaries from medical reports using LLMs such as Grok 3, Gemini 2.5 Flash and ChatGPT4.0 Mini, to speed up medical insurance premium calculation by 70%.
• Libraries used: OlmOCR, OpenAI, Flask and Puppeteer.
• Technologies used: Python, Node.js, Firebase, DigitalOcean, Docker and Git.

Manufacturing Ticket Resolution Agentic AI Assistant (07/2024 to 12/2024):

• Designed and developed RAG-based AI agent to speed up customer incidents resolution by 40%.
• Libraries used: Langchain, ChatOpenAI, OpenAIEmbeddings, HanaDB, Streamlit, and FastAPI.
• Technologies used: Python, Databricks, Cloud Foundry, SAP HANA, SQL, Kubernetes, Docker and Git.

Customer Experience Improvement Through Emotion and Personality Trait Detection (01/2024 to 06/2024):

• Designed and implemented novel hybrid deep learning-based NLP models (CNN+bi-LSTM/CNN+bi-GRU) for improved text-based emotion and personality trait classification, resulting in better end-user product innovation and experience with 15% increase in sales.
• Libraries used: Flask, NLTK, Keras, Gensim (FastText, Word2Vec), GloVe, Sklearn and Tensorflow.
• Technologies used: Python, SQL, Kubernetes, Docker and Git.

Automated Ticket Classification and Routing for Banking Domain (03/2023 to 12/2023):

• Developed NLP based model, to automatically analyze and route product-related customer incidents to correct teams in real-time.
• Sped up incident resolution times by 20%.
• Python libraries used: Flask, Sklearn, Pandas, Wordcloud, NLTK and Spacy.
• Technologies used: Python, Databricks, SQL, Kubernetes and GIT.

Gesture-based Sentiment Analysis for Sales Domain (07/2022 to 02/2023):

• Developed deep learning-based machine learning model to capture customer sentiment based on real-time recognition of gestures, using pre-trained MobileNet model with GRU-based custom layers.
• Helped capture and communicate customer feedback to service providers faster, resulting in quicker remediation and increase in customer retention by 15%.
• Python libraries used: Flask, Tensorflow, Keras, CV2, SKImage, and Numpy.
• Technologies used: Python, SQL, Kubernetes and GIT.

Manufacturing Defect Detection (11/2021 to 06/2022):

• Implemented machine learning model (CNN+LSTM based model) for real time automatic visual product defect detection, during product manufacturing and shipping.
• Early defect detection reduced defective product delivery to customers to 0% and improved product quality and sales by 15%.
• Python libraries used: Flask, Tensorflow, Numpy, Pandas and Keras.
• Technologies used: Python, SQL, Kubernetes and GIT.

Logistics Delivery Prediction Feature (04/2021 to 10/2021):

• Built machine learning model using linear regression, to accurately predict delivery timings.
• This helped to implement dynamic routing mechanisms in real-time, thereby increasing on-time delivery of goods to customers by 25%.
• Python libraries used: Flask, Sklearn, Numpy, Pandas and Statsmodels.
• Technologies used: Python, SQL, Kubernetes and GIT.

Static Sales Forecasting for Automobile Industry (06/2020 to 03/2021):

• Developed machine learning model using linear regression, to better understand driving factors influencing sales and predict sales, thus improving overall sales through better risk mitigation.
• Increased overall automobile sales by 20%.
• Python libraries used: Flask, Sklearn, Numpy, Pandas and Statsmodels.
• Technologies used: Python, Flask, SQL, Kubernetes and GIT.

General Experience:

SAP Development Landscape Management - Feature Based Engineering (04/2025 - present) :

• End-to-end product design and development of checks in code pipelines of continuous feature-based enhancements delivered for the S4 HANA Public Cloud product.
• Reduced continuous feature enhancements' delivery times by 30%.
• Technologies worked with: Java, Python, OOABAP, 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 archiving process, to replicate manufacturing data from analytics data store (warm store) to 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 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 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 caught during development and reduced manual test effort 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: OOABAP, Node.js, Java, JMeter, Sahi Pro, Docker, Kubernetes and Git.

• Worked on offshore SAP implementation projects for customers Celestica Inc., Federal Mogul Powertrain and Yara International.
• Developed custom objects for SAP modules such as SAP PM, SAP SD and SAP QM to simplify business processes.
• Technologies worked with: SAP ABAP RICEF.

• Worked on on-site and offshore implementation, roll-out and support projects for customers: Vienna Insurance Group, RAK Ceramics India, MPP Technologies and The Children's Place.
• Provided support for and developed custom objects for SAP modules such as SAP FSPM, SAP SD and SAP MM to improve business processes.
• Technologies used: SAP ABAP RICEF and OOABAP.

• Worked on offshore implementation, support and in-house tool development projects for General Motors Company, Abbott Laboratories and Mphasis - EAS Pre-sales and Solutions.
• Developed and provided support for custom objects in SAP modules such as SAP HCM, SAP FI and SAP SD.
• Technologies worked with: SAP ABAP RICEF and OOABAP.