We are looking for an innovative AI Engineer who will design, develop, and deploy artificial intelligence (AI) and data analytics-based applications aligned with our organization's strategic goals.

Qualifications:

• Bachelor's degree in Computer Engineering, Software Engineering, Data Science, or related fields
• Minimum 3 years of experience in AI and data analytics.
• Advanced programming skills in Python, R, or related languages.
• Experience in RAG architectures, agentic chatbot systems, and LLM models.
• Knowledge of vector databases (Pinecone, Qdrant, Pgvector, etc.).
• Experience with workflow tools such as Flowise, n8n, or similar.
• Competency in API development and microservice architectures.
• Proficiency in SQL, including querying, creating views/tables, functions, and stored procedures
• Strong problem-solving, analytical thinking, and communication skills.
• Fluent in English, both written and spoken.

Responsibilities:

• Develop Retrieval-Augmented Generation (RAG) architectures and agentic chatbot systems, integrating them with existing corporate information systems.
• Utilize vector databases such as Pinecone, Qdrant, and Pgvector to create data indexing, semantic search, information retrieval, and recommendation systems.
• Design, develop, and implement corporate process automation solutions using workflow automation tools such as Flowise, n8n, and similar platforms.
• Manage fine-tuning or training processes of large language models (LLM) tailored to organizational needs; actively participate in data collection, preprocessing, model training, and evaluation phases.
• Collect and analyze structured and unstructured data from various sources to generate actionable insights that add value to the organization.
• Participate in API development, microservices, and integration processes for AI and data analytics applications; ensure the sustainability, performance, and security of systems.
• Create decision-support systems using data visualization and reporting tools, and provide data-driven reports to management and stakeholders.
• Continuously monitor the performance of applications and models, develop optimization suggestions, and manage improvement processes.
• Prepare technical documentation for AI and data projects, documenting processes and integration steps in detail.
• Stay updated on current AI and data analytics technologies and methodologies, evaluate their applicability within the organization, and develop pilot projects.
• Share knowledge within the team, providing technical consultancy and guidance as needed.
• Build and maintain data pipelines to connect various data sources and manage the ELT (Extract, Load, and Transform) process using SSIS.
• Develop business performance visualizations and dashboards using SSRS to report on daily, weekly, and monthly activities, identify KPIs, and monitor progress.
• Foster a culture of data-driven decision-making by promoting objectivity over subjectivity and ensuring opinions are not mistaken for facts.
• Adhere strictly to information security, data privacy, and ethical principles, complying fully with relevant legal and corporate standards.