Artificial Intelligence Manager

Job Description

We are looking for an Artificial Intelligence Manager who will take a leadership role in AI, data analytics, predictive modeling, and intelligent automation projects, and who can lead the development of end-to-end AI solutions without relying on RPA tools.

In this role, we expect a technology leader who can translate diverse business challenges into data- and AI-driven solutions, guide teams in processing and modeling the right data using the right methods, and ensure that developed AI capabilities are integrated into decision-making processes and enterprise systems.

The ideal candidate will be responsible for AI strategy, project prioritization, technical direction, solution architecture, team leadership, and successful delivery of AI initiatives from concept to production.

We are seeking candidates with a Bachelor’s or Master’s degree in Computer Engineering or related fields, with 8+ years of professional experience across software engineering, data engineering, AI/ML, or related technology domains, including strong hands-on experience in AI/ML and Generative AI technologies and a proven track record of technical leadership, project leadership, or team management.

Responsibilities

• Translate business needs from internal stakeholders into data-driven and AI-driven problem definitions and solution strategies
• Define and prioritize AI use cases, projects, and technology initiatives based on business value and feasibility
• Lead data collection, cleaning, feature engineering, and analysis activities
• Design and oversee ETL / ELT processes and data pipelines
• Guide the selection and application of appropriate models for regression, classification, time series forecasting, and anomaly detection
• Collaborate with internal stakeholders to analyze business processes and re-architect them using an AI-native automation approach
• Lead the development and implementation of LLM-based AI and intelligent automation solutions
• Drive the use of NLP, LLM, RAG, and Generative AI approaches for relevant business use cases
• Lead solutions for document understanding, OCR, text processing, and intelligent classification using NLP, LLM, and RAG approaches
• Guide the development of container-based services using Docker or similar technologies
• Ensure the integration of LLM and ML models into enterprise systems via APIs, services, and event-driven architectures
• Establish and maintain appropriate AI/ML architecture and engineering standards
• Train, test, validate, and evaluate the performance of models and AI solutions
• Lead the transition of AI initiatives from PoC and pilot implementations into production environments
• Analytically design how model outputs translate into business decisions and measurable business outcomes
• Report developed models and AI solutions, produce interpretable outputs, and maintain proper documentation
• Lead and develop AI, ML, Data Engineering, and Software Engineering teams where applicable
• Define technical priorities, resource requirements, project plans, and delivery milestones
• Take AI projects from concept to production end-to-end
• Identify and improve performance bottlenecks, reliability issues, architectural constraints, and system design gaps
• Apply and establish MLOps / LLMOps practices, including model versioning, monitoring, drift detection, evaluation, and retraining workflows
• Ensure compliance with KVKK / GDPR, enterprise data security, and responsible AI principles
• Communicate project status, risks, technology decisions, and business outcomes to senior management and relevant stakeholders
• Identify opportunities to improve the organization's AI maturity, technology capabilities, and business processes

Technical Qualifications

• Advanced development experience with Python
• Experience leading AI- and software-driven automation solutions without using RPA tools
• Hands-on experience designing and developing LLM-based AI and Generative AI solutions
• Experience with NLP, RAG, prompt engineering, or LLM evaluation is preferred
• Experience with OCR, document processing, or unstructured data pipelines
• Experience working with SQL and NoSQL databases
• Strong knowledge of NumPy, Pandas, SciPy, and related ML / NLP libraries
• Solid understanding of supervised and unsupervised learning and predictive modeling
• Experience with regression, classification, clustering, anomaly detection, and time series analysis
• Strong knowledge of model evaluation metrics (Accuracy, Precision, Recall, F1, RMSE, etc.)
• Ability to translate business requirements into technical AI solutions and strategies
• Strong analytical thinking, problem-solving, and technical decision-making skills
• Strong understanding of overfitting / underfitting and model improvement techniques
• Ability to design and implement RESTful, API-first integrations
• Understanding of event-driven architectures and enterprise system integrations
• Experience with Docker or similar containerization technologies is preferred
• Understanding of how model outputs integrate into business processes and decision-making
• Experience with MLOps / LLMOps, model monitoring, and AI lifecycle management
• Awareness of data privacy regulations (KVKK / GDPR), AI governance, responsible AI, and enterprise data security principles
• Ability to evaluate technical approaches and make architecture and technology decisions aligned with business objectives