Senior Artificial Intelligence Engineer

Responsibilities

• Design, develop, and optimize recommendation systems, including collaborative filtering, content-based methods, and deep learning-based approaches.
• Build and apply algorithms for user behavior analysis, segmentation, and personalization.
• Develop RAG-based information retrieval systems.
• Fine-tune and deploy commercial & open-source LLMs.
• Design data acquisition and labeling processes, and develop data quality control algorithms.
• Lead AI projects, mentor junior team members, and collaborate effectively with interdisciplinary teams.
• Stay updated on state-of-the-art technologies and deliver proofs-of-concept (PoCs) to evaluate their relevance to product requirements.
• Understand business objectives and develop models to achieve them, along with metrics to track progress.
• Participate in design and code reviews, facilitate discussions, and align on architectural and strategic decisions.

Qualifications

• BSc degree in Computer Engineering, Industrial Engineering, Mathematical Engineering, Artificial Intelligence, or related fields.
• At least 4 years of experience in data science and MLOps.
• Strong proficiency in Python programming and data science libraries.
• Familiarity with recommendation systems algorithms and concepts.
• Experience in modeling for NLP tasks.
• Hands-on experience with frameworks for building and integrating LLM-based applications.
• Solid understanding of Retrieval-Augmented Generation (RAG).
• Experience with SQL/NoSQL and database structures.
• Familiarity with CI/CD processes.
• Experience with Docker and Kubernetes technologies.
• Experience with PyTorch, TensorFlow, XGBoost, CatBoost, LightGBM, Hugging Face, and OpenAI tools.
• Proficiency in written and spoken English.
• Ability and motivation to self-teach, enter new domains, and manage ambiguity.

Preferred / Nice-to-have Qualifications

• A MSc degree or Ph.D. in a relevant field.
• Solid academic or industrial experience in recommendation systems, user profiling, and human-computer interaction.
• Experience with Kubeflow technology.
• Familiarity with modern big data solutions and distributed computing platforms.
• Hands-on experience with vector and graph database technologies.
• Experience with cloud-based AI services.
• Hands-on experience with LangChain, LLamaIndex, or similar frameworks and libraries.
• Understanding of explainable AI concepts and applications.
• Experience in one of the following areas—knowledge bases, network analysis, Graph Neural Networks (GNNs), or Reinforcement Learning (RL)—is desirable.

Interview Process

1. Phone call for invitation
2. Technical Test
3. Online English and Technical Interview
4. Final Interview

Important Note

Applicants must have at least 4 years of academic or professional experience in AI after graduation from relevant degrees.