Spoorthy Siddannaiah-Machine Learning Research Engineer
Check rate
Experience
Machine Learning Research Engineer
Fraunhofer EMFT
- 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
Research Assistant
Fraunhofer EMI/Fraunhofer IEE
- 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
Student Research Assistant
Ulm University
- 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.
Business area experience
See which departments and functions this freelancer has contributed to most.
Experienced in Information Technology, Research and Development, and Product Development.
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
Education
University of Ulm
Master of Science, Communication and Information Technology · Communication and Information Technology · Ulm, Germany
National Institute of Engineering
Bachelor of Engineering, Electronics and Communication Engineering · Electronics and Communication Engineering · India
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
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.
The availability of Spoorthy needs to be confirmed.
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The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
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.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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