Principal AI Architect

Intellias is a company working with global organizations to deliver advanced AI solutions that transform business processes and enable next-generation digital experiences.

• 5+ years of experience in AI/ML, data science, or distributed systems engineering.
• Proven experience designing and delivering production-grade AI solutions at enterprise scale.
• Hands-on experience with LLM systems, AI agents, agentic RAG, tool/function calling, and multi-agent architectures.
• Experience with cloud AI platforms such as AWS Bedrock or AgentCore.
• Experience with LLM evaluation, monitoring, observability, and production tracing.
• Understanding of AI security concepts including enterprise identity, OAuth/Entra, role-based access control, permission-aware retrieval, and auditability.
• Strong Python skills.
• Experience with SQL and NoSQL databases.
• Knowledge of modern cloud-native technologies including Docker, Kubernetes, CI/CD pipelines, Infrastructure as Code, and MLOps frameworks.
• Experience working with distributed/global teams.
• Ability to work directly with clients and advise stakeholders on AI strategy and architecture.
• Experience using AI coding assistants (e.g., Claude Code, Copilot) and promoting AI-assisted engineering practices.
• English proficiency at B2/C1 level.

Nice to have:

• Experience with Databricks / Unity Catalog or enterprise lakehouse environments.
• Experience migrating AI platforms or modernizing existing AI ecosystems.
• Experience defining architecture governance practices and technical documentation standards.
• Background in consulting or technology advisory roles.

• Design and implement enterprise-scale AI/ML solutions across multiple business domains.
• Lead architecture and development of production-grade LLM and agentic AI systems.
• Build and evolve Generative AI solutions including Retrieval-Augmented Generation (RAG), AI agents, tool/function calling, and multi-agent orchestration.
• Define AI architecture standards, MLOps practices, governance models, and deployment strategies.
• Design scalable AI platforms supporting model portability and flexible LLM integration.
• Work with cloud AI platforms such as AWS Bedrock or equivalent solutions.
• Establish AI evaluation, monitoring, observability, and quality assurance strategies.
• Design secure AI platforms with identity management, access control, and auditability.
• Support AI platform modernization and migration initiatives.
• Integrate AI platforms with enterprise data ecosystems and lakehouse environments.
• Ensure AI infrastructure reliability, scalability, performance, and cost efficiency.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders.
• Provide technical leadership, mentoring, and guidance to engineering teams.
• Communicate complex technical concepts to technical and executive audiences.

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