Machine Learning Engineer – LLM & Generative AI
Doktar Technologies is a leading Agritech company with a mission to revolutionize food systems through intensive use of data and data-driven decisions. Headquartered in the Netherlands with offices in Wageningen, Istanbul and Izmir, Doktar delivers a comprehensive suite of digital services across the entire agricultural value chain. Positioned as a one-stop shop for digital agriculture solutions, Doktar embraces emerging technologies such as remote sensing, internet of things, machine learning, and artificial intelligence.
For our growing Data Science team we are looking for a Mid-Senior Machine Learning Engineer. You’ll design, build, and deploy production-grade AI systems that deliver measurable impact on agriculture and sustainability. This role suits engineers who enjoy solving complex, real-world problems and turning research into reliable systems at scale. You’ll work across computer vision, large language models, and MLOps depending on your background, while mentoring peers and helping shape Doktar’s growing AI stack. Our environment is fast-moving, research-informed, and deeply mission-driven.
What you’ll do
Lead projects across the ML lifecycle: design and implement data, training, inference, and monitoring pipelines with CI/CD, versioning, and rollback paths.
Build reliable and scalable ML systems: focusing on latency, cost, observability, and automated retraining.
Translate research into production: turning experimental models into maintainable, testable, and well-monitored services.
Collaborate cross-functionally with data, platform, and product teams to align on metrics, SLAs, and success criteria.
Contribute to team learning by reviewing others’ work, sharing best practices, and promoting reproducible experimentation.
Ensure trustworthiness of deployed models via validation, drift detection, and continuous evaluation.
What we’re looking for
4–7 years of experience delivering ML models or infrastructure in production.
Strong Python skills; PyTorch preferred.
Solid understanding of deep learning architectures (CNNs, Transformers, multimodal systems).
Experience in at least one of: computer vision, large language models, or other foundation models.
Proven experience in production ML: model serving, optimization, versioning, and monitoring.
Familiarity with cloud platforms (Azure preferred), Docker, and workflow tools (Airflow, MLflow, Kubeflow, Azure ML).
Experience with Infrastructure-as-Code and automated MLOps practices.
Exposure to HPC environments or distributed training setups.
Systems mindset: ability to design for reliability, maintainability, and clear ownership.
Strong communication and collaboration skills.
What we offer
An interesting and highly autonomous job in a young and interdisciplinary team;
Participation in global-scale projects;
State-of-the-art technical facilities (software and communication tools);
Professional development opportunities;
Hybrid work;
Company shares options;
Private health insurance covering family members under 22 years;
Monthly lunch fee (vouchers) and transportation allowance;
Performance-based yearly bonuses.