Agentic AI Domain Architect
Carollo Engineers
Agentic AI Domain Architect
We are looking for a domain architect to shape the future of AI-powered, agentic-based workflow experiences across logistics, fleet, and mobility solutions. The role sits at the intersection of domain architecture, agent design, product development, and applied data science. Requires 5+ years of experience designing workflow-centric systems and strong background in domain or enterprise architecture.
Agentic AI Domain Architect
We are looking for a domain architect to shape the future of AI-powered, agentic-based workflow experiences across logistics, fleet, and mobility solutions. The role sits at the intersection of domain architecture, agent design, product development, and applied data science. Requires 5+ years of experience designing workflow-centric systems and strong background in domain or enterprise architecture.
Salary
Core Qualifications
Technical (Must-have)
Soft Skills
Key Responsibilities
- Design and own agentic integration patterns between customer applications, enterprise systems, and agent frameworks (APIs, events, MCP-style tool interfaces, schemas, contracts).
- Define how agents invoke, coordinate with, and reason over external systems while respecting customer architecture, security, and governance constraints.
- Act as the architectural authority on how agentic capabilities are embedded into real customer environments, not as standalone copilots.
- Translate end‑to‑end business workflows into agent-compatible domain workflows, decomposed into modular, reusable agent skills.
- Define skill boundaries, preconditions, outputs, confidence signals, and failure modes.
- Ensure workflows support automation, human-in-the-loop, escalation, and explainability by design.
- Align domain workflows with multi-agent or hierarchical agent orchestration models where needed.
- Design domain-specific context models that combine operational data, spatial/temporal state (where relevant), user intent, and historical interactions.
- Define what agents should remember, forget, summarize, or abstract over time (short-term, long-term, episodic memory).
- Drive a data-centric approach to agent improvement by extracting signals from agent memory, user interactions, workflow outcomes, corrections, and overrides.