AI Architect

About the role

TITAN Group seeks an experienced AI Architect to lead all AI and GenAI initiatives worldwide. Based at the headquarters in Athens, the role bridges strategic architecture with hands‑on AI engineering, driving business transformation through scalable, production‑grade solutions.

Key responsibilities

• Design an enterprise‑wide AI orchestration architecture, including blueprints for AI platforms, RAG pipelines, LLM orchestration layers, and agent workflows.
• Translate business and regulatory requirements into secure, scalable technical designs and reference architectures.
• Develop and integrate production‑quality Python code, MVPs, PoCs, and full AI solutions across enterprise applications, APIs, and data platforms.
• Implement agentic AI systems, multi‑agent orchestration, memory management, and autonomous decision workflows.
• Build observability stacks for prompt monitoring, model response tracking, and vector retrieval quality.
• Manage model lifecycle: versioning, drift detection, evaluation‑driven retraining, and bias mitigation.
• Optimize inference cost, latency, and throughput while ensuring compliance with security, IAM, and encryption standards.
• Collaborate with FinOps to monitor and control AI usage costs (cloud compute, LLM tokens, licensing).
• Provide technical guidance to senior leadership, mentor engineering teams, and champion responsible AI practices.

Required profile

• Proven experience leading AI architecture and implementation at an enterprise scale.
• Deep understanding of generative AI, large language models, Retrieval‑Augmented Generation (RAG), and agentic AI concepts.
• Strong background in AI governance, model risk management, and regulatory compliance.
• Ability to work cross‑functionally with technology, cybersecurity, data management, and digital solutions teams.

Required skills

• Python programming and related AI/ML frameworks.
• Design and deployment of AI pipelines, LLM orchestration, and RAG architectures.
• Model lifecycle management, including versioning, drift detection, and bias mitigation.
• Observability and monitoring of AI systems (prompt tracking, performance metrics).
• Enterprise security standards (IAM, encryption) and AI governance frameworks.