Artificial Intelligence Manager

Job DescriptionWe 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.ResponsibilitiesTranslate business needs from internal stakeholders into data-driven and AI-driven problem definitions and solution strategiesDefine and prioritize AI use cases, projects, and technology initiatives based on business value and feasibilityLead data collection, cleaning, feature engineering, and analysis activitiesDesign and oversee ETL / ELT processes and data pipelinesGuide the selection and application of appropriate models for regression, classification, time series forecasting, and anomaly detectionCollaborate with internal stakeholders to analyze business processes and re-architect them using an AI-native automation approachLead the development and implementation of LLM-based AI and intelligent automation solutionsDrive the use of NLP, LLM, RAG, and Generative AI approaches for relevant business use casesLead solutions for document understanding, OCR, text processing, and intelligent classification using NLP, LLM, and RAG approachesGuide the development of container-based services using Docker or similar technologiesEnsure the integration of LLM and ML models into enterprise systems via APIs, services, and event-driven architecturesEstablish and maintain appropriate AI/ML architecture and engineering standardsTrain, test, validate, and evaluate the performance of models and AI solutionsLead the transition of AI initiatives from PoC and pilot implementations into production environmentsAnalytically design how model outputs translate into business decisions and measurable business outcomesReport developed models and AI solutions, produce interpretable outputs, and maintain proper documentationLead and develop AI, ML, Data Engineering, and Software Engineering teams where applicableDefine technical priorities, resource requirements, project plans, and delivery milestonesTake AI projects from concept to production end-to-endIdentify and improve performance bottlenecks, reliability issues, architectural constraints, and system design gapsApply and establish MLOps / LLMOps practices, including model versioning, monitoring, drift detection, evaluation, and retraining workflowsEnsure compliance with KVKK / GDPR, enterprise data security, and responsible AI principlesCommunicate project status, risks, technology decisions, and business outcomes to senior management and relevant stakeholdersIdentify opportunities to improve the organization's AI maturity, technology capabilities, and business processesTechnical QualificationsAdvanced development experience with PythonExperience leading AI- and software-driven automation solutions without using RPA toolsHands-on experience designing and developing LLM-based AI and Generative AI solutionsExperience with NLP, RAG, prompt engineering, or LLM evaluation is preferredExperience with OCR, document processing, or unstructured data pipelinesExperience working with SQL and NoSQL databasesStrong knowledge of NumPy, Pandas, SciPy, and related ML / NLP librariesSolid understanding of supervised and unsupervised learning and predictive modelingExperience with regression, classification, clustering, anomaly detection, and time series analysisStrong knowledge of model evaluation metrics (Accuracy, Precision, Recall, F1, RMSE, etc.)Ability to translate business requirements into technical AI solutions and strategiesStrong analytical thinking, problem-solving, and technical decision-making skillsStrong understanding of overfitting / underfitting and model improvement techniquesAbility to design and implement RESTful, API-first integrationsUnderstanding of event-driven architectures and enterprise system integrationsExperience with Docker or similar containerization technologies is preferredUnderstanding of how model outputs integrate into business processes and decision-makingExperience with MLOps / LLMOps, model monitoring, and AI lifecycle managementAwareness of data privacy regulations (KVKK / GDPR), AI governance, responsible AI, and enterprise data security principlesAbility to evaluate technical approaches and make architecture and technology decisions aligned with business objectives