AI Architect

So, what are the main responsibilities of the AI Architect in Netcompany? 

Joining us as the AI Architect, you will own the technical vision and architecture of GenAI and LLM solutions for enterprise clients. You will be responsible for designing robust, scalable, and future-proof AI systems that translate business requirements into high-quality technical architectures and production-ready solutions.

This is a senior, client-facing technical role. Your ability to design enterprise-grade AI architectures, make sound technology choices, and ensure architectural integrity across complex environments will be critical. You will work closely with senior stakeholders, product owners, and engineering teams, acting as the go-to authority on AI system design, architecture patterns, and technical best practices.

Your primary focus will be the design and implementation of advanced GenAI and LLM architectures. You will define end-to-end solution blueprints, including model integration, RAG pipelines, data flows, security, scalability, observability, and governance. You will ensure that solutions are modular, reusable, extensible, and aligned with enterprise architecture standards.

As the AI Architect, you will:

• Own the end-to-end technical architecture of GenAI and LLM solutions across multiple enterprise projects.
• Translate business and functional requirements into scalable, secure, and production-ready AI architectures.
• Design advanced GenAI pipelines including RAG architectures, orchestration layers, evaluation frameworks, and model deployment strategies.
• Define integration patterns between LLM systems and enterprise platforms (CRM, ERP, data platforms, APIs, legacy systems).
• Establish architectural standards, best practices, and reusable frameworks for AI delivery.
• Ensure non-functional requirements are addressed, including scalability, performance, security, compliance, and cost efficiency.
• Guide engineering teams on implementation decisions, conducting architectural reviews and ensuring technical excellence.
• Evaluate and select appropriate tools, frameworks, and cloud services aligned with enterprise constraints and long-term strategy.
• Drive AI governance, observability, monitoring, and responsible AI practices at architectural level.
• Contribute to pre-sales activities by shaping solution architectures, estimating complexity, and assessing technical feasibility.