Aniket Kulkarni

About

AI/ML and generative AI developer with experience building end-to-end machine learning pipelines, LLM-based applications, and scalable backend services. Recently completed a Master’s degree in Artificial Intelligence in Germany and is ready for immediate employment.

Experience

• Master's Thesis – Applied AI Engineer
  Robert Bosch GmbH · Renningen, Germany
  Apr 2025 – Sep 2025

  Built a production-ready end-to-end AI pipeline in Python, including model training, optimisation, inference deployment, and systematic evaluation — 25% efficiency improvement over the baseline. Designed and implemented a performance evaluation framework to measure accuracy, latency, and resource efficiency across multiple model configurations — directly relevant to monitoring practices in LLMOps. Applied TDD-oriented validation tools and clean architecture principles in a large, maintainable codebase contributed to the EU consortium CONVOLVE. Worked closely with Bosch Research and consortium partners to translate technical requirements into production-ready functional increments.

• Software Engineer – Enterprise Systems
  NTT DATA · Pune, India
  Sep 2021 – Feb 2023

  Developed production-grade REST APIs and backend services with error handling, retry logic, asynchronous processing, and SLA monitoring for enterprise B2B SaaS clients. Integrated multiple third-party platforms via API orchestration workflows and reduced client-side processing time by 40%. Collaborated with engineering and product teams in an agile, cross-functional environment; translated business requirements into technical implementations. Maintained and extended a complex production codebase with peer code reviews and clean architecture standards.

• Product Development Intern
  Techniken Technologies · Pune, India
  Feb 2021 – Apr 2021

  Developed IoT firmware for real-time sensor data acquisition using ESP8266 and NodeMCU. Integrated firmware outputs into web-based visualisation dashboards for real-time data monitoring.

• Project Intern
  Sisai Technologies · Pune, India
  Nov 2020 – Feb 2021

  Built a sensor fusion pipeline to combine IMU and GPS data for navigation systems. Implemented Kalman filters for real-time localisation under noisy sensor conditions.

Education

• Master of Science
  University of Applied Sciences Würzburg-Schweinfurt (THWS)
  Artificial Intelligence · Mar 2023 – Mar 2026
• Bachelor of Technology
  Dr. Vishwanath Karad MIT World Peace University
  Electronics and Communication Engineering · Aug 2019 – May 2022

Skills

• SQL
• Computer Vision
• Deep Learning
• FastAPI
• Git
• Python
• Machine Learning
• Prompt Engineering
• MySQL
• NumPy
• Pandas
• Streamlit
• scikit-learn
• OpenCV
• PostgreSQL
• Flask
• PyTorch
• TensorFlow
• AWS
• Docker
• Kubernetes
• LangChain
• Generative AI
• REST APIs
• Natural Language Processing
• Azure
• GCP
• RAG
• LLMs

Projects

• Reinforcement Learning for an Optimal Scheduling Strategy
  Python

  Designed a configurable data and simulation pipeline with 8 agent configurations across 100,000 episodes, including automated metric collection, results logging, and systematic performance evaluation across all variants. Published at IEEE 2025.

• Airbelts
  Computer Vision, Python

  Built a production AI service for real-time computer vision classification with 82% accuracy; reviewed under the German federal EXIST programme.

• Dog Activity Detection and Classification
  ETL, Machine Learning, CNN, Streamlit, REST API

  Built a fully automated ETL and ML pipeline for ingesting raw sensor data, preprocessing, segmentation, CNN model training, validation, and Streamlit web deployment via a REST API. Processed 16,048 real-world data points across 5 classification targets with full test coverage; published at ICDD 2024.

• Transformer-based Conversational AI
  BERT, GPT-3, Prompt Engineering, REST API

  Fine-tuned BERT for intent classification with 92.5% accuracy on 220,579 conversation examples and integrated GPT-3 with structured prompt engineering and context-aware memory for agent-based multi-turn response generation. Deployed via a REST API with automated test coverage and evaluation metrics; published at IJRASET 2021.

Courses & certifications

• MuleSoft Certified Developer Level 1 · Enterprise integration

🗣️ Languages

• German · Intermediate
• English · Fluent
• Hindi · Fluent

📚 Publications

• Optimal Blackjack Strategy with Reinforcement Learning · 2025
• Dog Activity Detection and Classification · 2023
• Evaluation of a Transformer Model for Conversational Chatbots · 2021

🎯 Hobbies & interests

• Research
• AI
• Machine Learning