AI Solution Architect

About the role

Prometa.ai is seeking an AI Solution Architect to lead the design and deployment of scalable, production‑ready AI infrastructures. The role bridges cutting‑edge research on large language models and agentic AI with real‑world enterprise applications across finance, insurance, telecom, and other sectors.

Key responsibilities

• Design, implement and optimise multi‑agent AI systems that combine LLMs, Retrieval‑Augmented Generation (RAG) and autonomous planning/reasoning.
• Engage with clients on‑site and virtually to understand business challenges, propose AI transformation opportunities and deliver proof‑of‑concept presentations.
• Integrate AI solutions with legacy environments (mainframe, SOAP, proprietary APIs) and cloud platforms (AWS, Azure, GCP), communicating strategies to C‑level and IT leaders.
• Build robust data ingestion pipelines, embedding‑based retrieval, and vector/graph database architectures for real‑time reasoning over structured and unstructured data.
• Ensure compliance with GDPR, KVKK, MASAK, PCI‑DSS, Basel III and related security frameworks, translating requirements for non‑technical stakeholders.
• Oversee containerised, GPU‑optimised deployments on Kubernetes or OpenShift, guaranteeing performance, fault tolerance and cost efficiency for thousands of concurrent sessions.
• Collaborate with data engineers, compliance teams and business units to identify AI‑driven improvement areas and lead co‑creation workshops.
• Establish monitoring, evaluation and governance processes for AI systems in production.

Required profile

• Proven experience architecting enterprise AI solutions, preferably in regulated industries.
• Strong ability to translate complex technical concepts into clear business value for diverse audiences.
• Excellent communication skills for client‑facing and executive presentations.
• Track record of delivering AI projects from prototype to production at scale.

Required skills

• Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG)
• Agentic AI and autonomous workflow design
• Cloud platforms: AWS, Azure, Google Cloud Platform (GCP)
• Container orchestration: Kubernetes, OpenShift
• GPU‑optimised deployment and Docker
• Vector and graph databases
• Legacy system integration: mainframe, SOAP, proprietary APIs
• Regulatory compliance: GDPR, PCI‑DSS, Basel III (knowledge)