Artificial Intelligence Engineer

About the role

The company is looking for an Artificial Intelligence Engineer who takes an active part in AI, data analytics and prediction projects and builds end-to-end AI solutions without relying on RPA tools. The engineer turns diverse business problems into data- and AI-driven models, processes the right data with the right methods and ensures models are integrated into decision-making and enterprise systems.

Key responsibilities

• Translate business needs from internal stakeholders into data-driven problem definitions
• Collect, clean and prepare data, perform feature engineering and analysis
• Design and work with ETL / ELT processes and data pipelines
• Select models for regression, classification, time series forecasting and anomaly detection
• Collaborate with stakeholders to analyse business processes and re-architect them with an AI-native automation approach
• Train, test, validate and evaluate models; build LLM-based AI and automation solutions
• Develop solutions for document understanding, text processing and intelligent classification using NLP, LLM and RAG approaches (preferred)
• Build container-based services with Docker or similar, and integrate LLM and ML models into enterprise systems via APIs, services and event-driven architectures
• Turn model outputs into business decisions, report models with interpretable outputs and keep documentation
• Run PoCs and pilots, take projects from concept to production, and fix performance bottlenecks and reliability issues
• Apply MLOps practices such as model versioning, monitoring, drift detection and retraining workflows

Required profile

• Bachelor's or Master's degree in Computer Engineering or a related field
• At least 5 years of experience in data engineering and/or software development
• Ability to turn business requirements into technical AI automation solutions
• Awareness of data privacy regulations (KVKK / GDPR) and enterprise data security principles

Required skills

• Advanced Python development
• Hands-on experience with LLM-based AI solutions and OCR, document processing or unstructured data pipelines
• SQL and NoSQL databases
• NumPy, Pandas, SciPy and related ML / NLP libraries
• Supervised and unsupervised learning, regression, classification, clustering, anomaly detection and time series analysis
• Model evaluation metrics (accuracy, precision, recall, F1, RMSE) and RESTful API integrations