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Candidate understanding
Extract structured candidate information from CV data and preferences.
Machine Learning Engineer
Umut Gökdemir
Applied ML · NLP · ML Systems
I build and evaluate end-to-end machine learning systems across NLP, retrieval and predictive modelling, with a focus on measurable performance and reproducible engineering.
Featured work
ML Systems · Retrieval · Evaluation
CareerMatch Agent
An end-to-end ML-assisted job-matching system combining deterministic constraints, multilingual semantic retrieval, hybrid ranking and evidence-grounded LLM evaluation.
Python
LangGraph
FastAPI
SentenceTransformers
OpenAI
Gemini
Ollama
System workflow
01 → 0402
Hybrid retrieval
Combine deterministic constraints with multilingual semantic ranking.
03
LLM evaluation
Evaluate matches with evidence-grounded reasoning instead of opaque scoring.
04
Explainable output
Return ranked opportunities with clear supporting evidence and rationale.
Multi-provider architecture
Interchangeable OpenAI, Gemini and Ollama model providers.
Evaluation-driven
Benchmark tooling for ranking quality, retrieval metrics and latency.
Engineering-first
API-based design with automated tests and reproducible workflows.
Selected projects
Machine learning and performance engineering
A focused set of projects demonstrating end-to-end ML systems, rigorous model evaluation and performance-oriented engineering.
Applied Machine Learning
Predictive Maintenance ML
Built an end-to-end predictive-maintenance pipeline for imbalanced equipment-failure classification, combining domain-informed feature engineering, cross-validated model selection, threshold optimization and systematic error analysis.
Achieved 0.876 held-out test Average Precision with 0.947 precision and 0.794 recall.
Python
scikit-learn
XGBoost
Model Evaluation
Performance Engineering · Scientific Computing
GPU-Accelerated Satellite Constellation Simulation
Built and benchmarked a C++17/CUDA satellite simulation against serial CPU execution, with explicit CPU/GPU correctness validation and reproducible performance measurement.
Achieved 48.55× single-step kernel speedup and 46.84× end-to-end acceleration for a 1M-satellite, 100-step GPU-resident workload.
CUDA
C++17
Parallel Computing
Benchmarking
Technical toolkit
Tools I use to build, evaluate and engineer ML systems
My strongest work sits at the intersection of machine learning, NLP, retrieval and production-minded ML engineering.
Machine Learning
PyTorch
scikit-learn
pandas
NumPy
Deep Learning
Model Evaluation
Clustering
NLP & Retrieval
Hugging Face
Transformers
SentenceTransformers
Semantic Search
Embeddings
Named Entity Recognition
LLM Systems
LangGraph
Agentic Workflows
LLM Evaluation
Evidence-Grounded AI
OpenAI
Gemini
Ollama
ML Engineering
Python
FastAPI
Pydantic
REST APIs
HTTPX
Streamlit
Testing & Code Quality
pytest
Unit Testing
Integration Testing
mypy
Ruff
Type Checking
Infrastructure & Development
Docker
Git
GitHub Actions
CI/CD
Linux
AWS
Professional experience
Machine Learning Experience
Applied machine learning experience across NLP and high-dimensional scientific data, spanning data preparation, model development, evaluation and reproducible experimentation.
Machine Learning Engineer Intern
EnlightyAI
May 2025 – October 2025
Remote
- Improved span-level micro-F1 from 0.44 to 0.75 by fine-tuning and evaluating transformer-based NER models for noisy Turkish social-media text.
- Improved training-data quality across 6,000 annotated tweets by coordinating annotation and validation workflows, refining preprocessing and systematically investigating annotation and prediction errors.
- Strengthened model selection and experimentation by benchmarking multiple BERT and XLM-R variants through reproducible training, validation and error analysis.
- Led an 8-person internship team by planning workloads, assigning tasks, running weekly progress meetings and consolidating progress reports, helping keep annotation, preprocessing, training and evaluation work aligned across the project.
Machine Learning Engineer Intern
Functional Genomics Laboratory Regensburg
March 2024 – June 2024
Regensburg, Germany
- Reduced the analysis data footprint by approximately 40% by building a Python preprocessing pipeline for cleaning, restructuring and optimizing high-dimensional DESI mass-spectrometry imaging data.
- Preserved 95% of explained variance while reducing feature dimensionality by applying PCA before unsupervised tissue-region segmentation with four-cluster K-means.
- Improved the interpretability of unsupervised tissue segmentation by mapping cluster assignments back to spatial coordinates and validating molecular-density patterns across follow-up scan series.
- Worked within an interdisciplinary research team by communicating analysis progress through weekly reports, incorporating domain feedback into the workflow, presenting the methodology and results to the team and stakeholders.
Leadership & community
Leadership Beyond Engineering
Long-term experience in team leadership, mentoring, operations and community organization.
Scout Leader & Association President
Camadan Scout, Youth and Sport Association
April 2014 – April 2021
- Led the training and development of approximately 15–20 scouts each year by designing recurring educational programs, preparing training materials and delivering practical and theoretical sessions.
- Managed camps and community activities end to end, coordinating logistics, schedules, equipment, accommodation, safety and communication with participants and families.
- Directed association operations as President through volunteer coordination, delegation, budgeting, external partnerships and leadership meetings, while mentoring senior scouts into future leader and instructor roles.
Research & education
Quantitative foundations for machine learning
M.Sc. | 2026
Computational Science
University of Regensburg
Advanced training in machine learning, numerical methods, scientific computing and computational modelling, with hands-on work on data-driven and simulation-based problems.
B.Sc. | 2021
Physics
Middle East Technical University
Built a rigorous foundation in mathematical modelling, numerical problem solving and computational physics, including research experience in quantum many-body systems.
Contact
Open to machine learning opportunities
I am interested in Machine Learning Engineering, Applied ML and AI Engineering opportunities where I can work on meaningful models, data and production-oriented AI systems. For opportunities, collaborations or technical conversations, feel free to reach out.