Akıncan
Answers on my behalf
From model to production
I build labelled datasets, fine-tune language and vision models, compare checkpoints, and turn the selected model into an API that other systems can use.
I deploy event-driven services, APIs and data pipelines on GCP, then set up the permissions, secrets, storage and monitoring they need to run safely.
I turn messy source data into something models, analysts and products can trust, from ingestion and validation through embeddings, clustering and report generation.
I set up and operate Linux machines, private networking and protected service access alongside the AI and data workloads running on them.
Intero
I joined Intero as its founding machine learning engineer. I build the machine learning systems the product runs on, and I run the infrastructure they sit on.
What I worked on
LLM orchestration, Google Cloud and Linux operations
The platform the machine learning work at Intero runs on. I build the services the product calls, provision and run the machines underneath them, and keep the pipeline between them typed and resumable so a failed stage picks up where it stopped instead of starting over.
Two QwQ-32B LoRA fine-tunes trained on four H200 GPUs
The use case and dataset content are private, so this covers the model work only. I fine-tuned two QwQ-32B models for a classification system, from data preparation through training to the served endpoints. The system scored 93% on a benchmark kept out of training.
Async workers, GPU inference and repeatable Linux deployments
The long-running side of Intero. Jobs that chew through a lot of data over hours, on machines that have to recover without anyone watching them. I built the workers, the storage they write into and the GPU vision inference running on top.
Viable
I spent two years working remotely with a US team, moving between the data side and the model side of the product depending on what needed building.
What I worked on
GPU embeddings, high-memory clustering and model evaluation
Embeddings want a GPU and clustering wants memory, so I split them into two services instead of one that did neither well. I also built the tooling that decided which models and algorithms we actually shipped.
An eighteen-stage pipeline for inconsistent customer exports
Customers sent us files that agreed on nothing. Different columns, different date formats, different identifiers, different encodings. I built the pipeline that turned all of it into one shape the product could load.
Scheduling, parsing and delivery checks for customer reports
Reports went out on a schedule, and most of the work around them was manual and easy to get wrong. I automated the recurring parts and put checks in front of delivery so a bad report did not reach a customer.
Vipulse
I designed and built Vipulse’s multilingual corporate website and its publishing platform. The work covers the Next.js application, Payload CMS, PostgreSQL data model, lead capture, consent-aware analytics, search foundations and the release workflow.
I took the project from information architecture and visual system through implementation and deployment, with separate English and Hungarian content and a CMS workflow the company can operate directly.
What I worked on
A multilingual website and publishing system for electronics manufacturing
I designed and built Vipulse's production website, publishing platform and lead-capture system. The Next.js application serves separate English and Hungarian content from Payload CMS and PostgreSQL, with technical product pages, structured metadata, consent-aware analytics, spam controls and a preview-to-production release workflow.
Manisa Celal Bayar UniversityEducation
Four years of computer engineering fundamentals, including algorithms, operating systems, data analysis and artificial intelligence. I graduated as valedictorian with a 3.84 out of 4.00 GPA.
My graduation work included a custom wire protocol built directly on sockets and a tweet signal/noise classifier with data collection, annotation, transformer training and a small inference application.
What I worked on
Training an ALBERT model to separate signal from noise on Twitter
For my graduation project, I trained an ALBERT-xxlarge-v2 classifier to separate useful tweets from bots, marketing and unrelated posts around a trending topic. I built the collection pipeline, a human annotation interface, Firestore persistence, the PyTorch training and checkpoint flow, and a Flask API that served the selected model for batch classification.
Sapienza Università di RomaEducation
A semester in Rome studying machine learning, cybersecurity and software engineering. Living and studying abroad also gave me the chance to learn Italian and work in a new academic environment.
What I worked on
Public contributions from the past year
