Adithya K Ramesh

AI/ML Experience (01/2020 - present):

Medical Reports Summarization:

• Designed microservices to generate medical summaries from medical reports using LLMs such as Grok 3, Gemini 2.5 Flash and ChatGPT4.1 Mini, to speed up medical insurance premium and arbitrage potential calculations by 70%.
• Libraries used: OlmOCR, AWS Textract, OpenAI, Flask and Puppeteer.
• Technologies used: Python, Node.js, Firebase, DigitalOcean, Docker and Git.

Manufacturing Ticket Resolution Agentic AI Assistant:

• 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:

• 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, Datbricks, SQL, Kubernetes, Docker and Git.

Automated Ticket Classification and Routing for Banking Domain:

• 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:

• 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, Databricks, SQL, Kubernetes and GIT.

Manufacturing Defect Detection:

• 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, Databricks, SQL, Kubernetes and GIT.

Logistics Delivery Prediction Feature:

• 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, Databricks, SQL, Kubernetes and GIT.

Static Sales Forecasting for Automobile Industry:

• 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, Databricks, Flask, SQL, Kubernetes and GIT.

General Experience:

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

• End-to-end product design 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, 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 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: SAP ABAP, 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.