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Spoorthy Siddannaiah-Machine Learning Research Engineer

Spoorthy Siddannaiah
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Ulm, Germany

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Experience

Apr 2024 - Apr 2025
Munich, Germany

Machine Learning Research Engineer

Fraunhofer EMFT

Position Summary
Machine Learning Research Engineer at Fraunhofer EMFT
Industries
Manufacturing
Business Areas
Information Technology
Product Development
Research and Development
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  • Developed end-to-end predictive modeling pipelines for sensor data, improving Remaining Useful Life (RUL) estimation accuracy by 15%
  • Developed an active learning workflow with uncertainty sampling, reducing manual labeling by 30%
  • Used MLflow for experiment tracking, hyperparameter logging, and model versioning, ensuring reproducible training pipelines
  • Leveraged CI/CD tools (Jenkins, GitHub Actions) to automate deployment processes and reduce model release cycles
Sep 2022 - Feb 2024
Freiburg im Breisgau, Germany

Research Assistant

Fraunhofer EMI/Fraunhofer IEE

Position Summary
Research Assistant at Fraunhofer EMI/Fraunhofer IEE
Industries
Energy
Business Areas
Information Technology
Research and Development
  • Improved energy grid prediction accuracy by 12% using LSTM and CNN on multi-output time series data
  • Curated and preprocessed domain-specific datasets for fine tuning LLMs injury risk analysis
  • Fine-tuned open-source LLMs (BERT, LLaMA), achieving F1-score of 0.85 in injury risk classification
  • Built RAG workflows with LangChain and FAISS, improving QA accuracy by 15% via context-aware retrieval
  • Applied prompt engineering and evaluated LLM outputs, improving prediction consistency and reducing errors
  • Integrated Hugging Face and OpenAI APIs into enterprise applications
  • Automated CI/CD pipelines for reproducible training, versioning, and streamlined deployment
Jun 2022 - Aug 2022
Ulm, Germany

Student Research Assistant

Ulm University

Position Summary
Student Research Assistant at Ulm University
Industries
Education
Healthcare
Business Areas
Research and Development
  • Cleaned, preprocessed, and analyzed bioimpedance sensor time-series data using Pandas, NumPy, and SQL

Industry Experience

See where this freelancer has spent most of their professional time.

Experienced in Energy and Manufacturing.

Energy
Manufacturing
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Business Area Experience

See which departments and functions this freelancer has contributed to most.

Experienced in Information Technology, Research and Development, and Product Development.

Information Technology
Research and Development
Product Development
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Summary

Machine Learning Research Engineer with 2+ years of experience in deep learning, NLP, and LLMs. Specialized in building robust ML pipelines for real-world sensor systems and NLP applications, with strong expertise in LLM fine-tuning, RAG workflows, and scalable deployment.

Skills

  • Python
  • Sql
  • Azure
  • Aws
  • Docker
  • Ci/Cd Pipelines
  • Git
  • Linux
  • Nlp
  • Llms
  • Transformers
  • Rag
  • Reinforcement Learning
  • Pytorch
  • Tensorflow
  • Scikit-Learn
  • Pandas
  • Numpy
  • Matplotlib
  • Langchain
  • Hugging Face

Languages

English
Advanced
German
Intermediate

Education

Apr 2021 - Apr 2025

University of Ulm

Master of Science, Communication and Information Technology · Communication and Information Technology · Ulm, Germany

Aug 2015 - Aug 2019

National Institute of Engineering

Bachelor of Engineering, Electronics and Communication Engineering · Electronics and Communication Engineering · India

Statistics

Experience

Total positions 3
Experience in Energy 1.5 y
Avg length 10 m
Longest experience 1 y 5 m

Global Experience

Countries worked in 1 (Germany)
Primary country Germany

Expertise

Recent roles Machine Learning Research Engineer, Research Assistant, Student Research Assistant
Main industries Energy, Manufacturing
Main business areas Information Technology, Research and Development, Product Development

Qualifications

Highest degree Master

Profile

Created
Last Update

Frequently asked questions

Have questions? Find more information here.

Spoorthy is based in Ulm, Germany.

Spoorthy speaks the following languages: English (Advanced), German (Intermediate).

Spoorthy has at least 3 years of experience. During this time, Spoorthy has worked in at least 3 different roles and for 3 different companies. The average length of individual experience is 1 year and 11 months. Note that Spoorthy may not have shared all experience and actually has more experience.

Based on recent experience, Spoorthy would be well-suited for roles such as: Machine Learning Research Engineer, Research Assistant, Student Research Assistant.

Spoorthy's most recent position is Machine Learning Research Engineer at Fraunhofer EMFT.

In recent years, Spoorthy has worked for Fraunhofer EMFT, Fraunhofer EMI/Fraunhofer IEE, and Ulm University.

Spoorthy is most experienced in industries like Energy, Manufacturing, and Education. Spoorthy also has some experience in Healthcare.

Spoorthy is most experienced in business areas like Research and Development, Information Technology, and Product Development.

Spoorthy holds a Master in Communication and Information Technology from University of Ulm and a Bachelor in Electronics and Communication Engineering from National Institute of Engineering.

Spoorthy is immediately available full-time for suitable projects.

Spoorthy's rate depends on the specific project requirements. Please use the Meet button on the profile to schedule a meeting and discuss the details.

To hire Spoorthy, click the Meet button on the profile to request a meeting and discuss your project needs.

Daily Rate Distribution

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<€640 €800-960 €1280+

The rates shown represent the typical market range for freelancers in this position based on recent contracts on our platform.

Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.

Average rates for similar positions

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Daily rate avg. 851 €

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Median rate 840 €

The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.

Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.