Lead AI Architect

Scope

• Define the enterprise AI strategy, including commercial versus self-hosted open-source models, optimizing for cost, performance, security, and compliance.
• Architect large-scale autonomous multi-agent ecosystems, including orchestration, task delegation, context management, and agent-to-agent communication.
• Design the enterprise integration layer connecting AI agents to corporate systems through REST APIs, event-driven architectures, MCP, and custom connectors.
• Design distributed vector architectures and knowledge layers, including embedding pipelines, indexing strategies, metadata management, and RAG governance policies.
• Own security and compliance at the architectural level, including PII handling, authorization, access control, agent quality metrics, and adherence to the AI Act and other regulatory requirements.
• Drive production excellence by defining enterprise cloud architectures with full observability, automated evaluation, monitoring, cost governance, and Responsible AI guardrails.
• Act as the technical authority by establishing architectural standards, coordinating across DevOps, Security, and Business teams, and mentoring senior engineers and architects.

Skills

• 10+ years of experience in software architecture, software development, data engineering, or ML engineering, with deep hands-on expertise in GenAI and agentic AI.
• Proven track record of designing and delivering autonomous multi-agent systems at enterprise scale.
• Expert-level understanding of agentic architecture patterns, with the ability to define the technical direction for large engineering teams.
• Deep knowledge of model orchestration, inference cost optimization, and model selection across both commercial and open-source models.
• Experience architecting large-scale vector databases, embedding pipelines, and retrieval systems.
• Ability to design secure, privacy-preserving AI systems that are resilient to hallucinations, prompt injection attacks, and adversarial inputs.
• Expert-level experience in cloud architecture on AWS, Azure, or GCP.
• Deep expertise in LLMOps, including model versioning, evaluation pipelines, prompt management, cost monitoring, and CI/CD for AI solutions.
• Ability to define and establish AI-assisted development standards, governance frameworks, and engineering best practices across teams.