At Baykar, the leading force behind the "National Technology Initiative," we develop indigenous and national high-tech Unmanned Aerial Vehicle (UAV) systems and generate a global impact with our battle-proven platforms. With our end-to-end engineering approach—from R&D to production—we continue to sign off on projects that push the boundaries.

OUR DEPARTMENT:

The Natural Language Processing Team within the Digital System Software Technologies department is an R&D team focused on the research, development, and field deployment of large language models (LLMs) used in critical areas such as unique language models integrated into UAVs and other platforms, automation systems, and software assistants.

Our team develops original and innovative systems by performing deep development and improvements on language model technologies, producing field-applicable products by closely following current academic literature. By bridging academic knowledge with engineering discipline, our team provides solutions to real-world problems in the defense, aviation, and critical infrastructure sectors, transforming language technologies into operational systems with a focus on reliability and explainability.

POSITION OBJECTIVE:

The purpose of this position is to play an active role in the research, development, fine-tuning, and production-level integration of large language models and natural language processing technologies. Key objectives include enhancing the team's capacity to design, develop, and optimize AI-based language systems, and increasing the accuracy, reliability, and efficiency of existing model infrastructures.

WHAT AWAITS YOU:

• Conducting training studies on Transformer-based large language models.
• Designing, developing, and optimizing Retrieval-Augmented Generation (RAG) architectures by integrating vector databases, embedding models, and document management components.
• Developing end-to-end solutions for NLP tasks such as text classification, named entity recognition (NER), summarization, question-answering, dialogue management, and machine translation.
• Performing Inference optimization (quantization, pruning, distillation) to ensure model usability in real-time and hardware-constrained environments.
• Establishing LLMOps infrastructure, including experiment tracking, model versioning, A/B testing processes, and monitoring dashboards.
• Collaborating with interdisciplinary teams to integrate NLP/LLM components into software systems.

GENERAL QUALIFICATIONS:

• Graduated from (or currently an MA/PhD student in) Computer Engineering, Software Engineering, Artificial Intelligence, Electrical-Electronics Engineering, or equivalent engineering departments, or from positive sciences such as Mathematics, Physics, or Statistics.
• Advanced proficiency in Python and C++; a habit of writing clean, testable, and sustainable code.
• English proficiency at a level sufficient to read and understand technical documentation.
• Strong command of machine learning and deep learning fundamentals; conceptual and practical knowledge of neural network architectures.
• Knowledge and practical experience in core NLP tasks and techniques.
• Ability to analyze complex problems and generate solutions.
• Adaptability to a high-intensity work pace and flexible working hours.
• Strong team player with a high sense of responsibility.
• A multi-faceted work discipline.

ADDITIONAL QUALIFICATIONS (PREFERRED):

• Experience with Hugging Face Transformers, LangChain, LlamaIndex, or similar LLM frameworks.
• Experience working with vector databases such as FAISS, Chroma, Weaviate, Pinecone, or Milvus.
• Experience in fine-tuning using LoRA, QLoRA, PEFT, or instruction tuning techniques.
• Familiarity with model alignment methods such as RLHF, DPO, PPO, or similar.
• Experience managing model training, evaluation, and deployment processes using PyTorch or TensorFlow.
• Experience in deploying models to production environments using Docker, Kubernetes, or similar container and orchestration infrastructures.
• Knowledge of version control and continuous integration processes using Git and CI/CD tools.
• Proficiency in command line and scripting (bash/shell) in a Linux environment.
• Interest in Reliability, Explainability (XAI), or Responsible AI practices (hallucination reduction, bias detection).