About the job
Are you ready to lead the charge as an AI Architect at Netcompany? In this pivotal role, you will spearhead the technical vision and architectural framework for Generative AI and Large Language Model (LLM) solutions tailored for enterprise clients.
Your expertise will empower you to design resilient, scalable, and forward-thinking AI systems that convert business needs into exceptional technical architectures and production-ready implementations.
This is a senior-level, client-facing technical position where your capacity to architect enterprise-level AI solutions, make informed technological decisions, and uphold architectural integrity in complex settings will be paramount. You will collaborate closely with senior stakeholders, product owners, and engineering teams, establishing yourself as the trusted advisor on AI system design, architectural patterns, and technical best practices.
Your core responsibilities will include the design and execution of advanced Generative AI and LLM architectures. You will define comprehensive solution blueprints that encompass model integration, Retrieval-Augmented Generation (RAG) pipelines, data flows, security measures, scalability, observability, and governance. Your goal will be to ensure that solutions are modular, reusable, extensible, and adhere to enterprise architectural standards.
As the AI Architect, you will:
- Lead the end-to-end technical architecture of Generative AI and LLM solutions across various enterprise projects.
- Convert business and functional needs into scalable, secure, and production-ready AI architectures.
- Design sophisticated Generative AI 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 met, including scalability, performance, security, compliance, and cost efficiency.
- Guide engineering teams in implementation decisions, conducting architectural reviews to ensure technical excellence.
- Assess and select appropriate tools, frameworks, and cloud services that align with enterprise constraints and long-term strategies.
- Promote AI governance, observability, monitoring, and responsible AI practices at the architectural level.
- Contribute to pre-sales efforts by shaping solution architectures, estimating complexity, and assessing technical feasibility.

