Agentic AI Domain Architect

We're looking for a domain architect to shape the future of AI-powered, agentic-based workflow experiences across logistics, fleet, and mobility solutions. The Agentic Domain Architect is responsible for designing and governing the agentic interfaces between customer applications, enterprise systems, and our domain specific AI service and workflow layers.

This role sits at the intersection of domain architecture, agent design, product development and applied data science, working directly with customers, partners, presales and product team to translate complex customer workflows into domain-specific agent skills, context models, and interaction patterns.

You will define how agents understand domain reality, how they interact with customer systems, how workflows are decomposed into reusable skills, and how long-term agent memory and user interactions are converted into learning signals that continuously improve performance.

This is a hands-on role as part of the product team with strong ownership from concept * design * implementation * learning loop.

Key Responsibilities

1. Agentic Interface & Systems Architecture

• 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.

2. Domain-Specific Workflow & Skill Design

• 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.

3. Context, Memory & Domain Models

• 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).
• Ensure context grounding is deterministic, auditable, and aligned with domain semantics rather than prompt-only heuristics.

4. Learning Loops & Applied Data Science

• Drive a data-centric approach to agent improvement by extracting signals from: Agent memory, User interactions, Workflow outcomes, Corrections and overrides
• Partner with data science to transform these signals into training data, evaluation metrics, and optimization strategies.
• Influence model selection, grounding strategies, and reasoning approaches based on domain evidence rather than generic benchmarks.

What Success Looks Like

• Customer workflows are natively agentic, not retrofitted.
• Agent skills are reusable, observable, and continuously improving.
• Domain context and memory meaningfully improve decision quality over time.
• Data from real usage directly informs model, workflow, and skill evolution.
• The organisation develops durable agentic IP at the domain level

You are architecture-first thinker who remains pragmatic, delivery-oriented and is comfortable operating in ambiguity and shaping new patterns rather than applying existing playbooks. You have strong communication skills, able to align technical depth with business and operational context., * Bachelor's or Master's degree in computer science, software engineering, cognitive/data science or a related field.

• 5+ years of experience designing workflow-centric systems (not just APIs or microservices), with several years focused on AI-driven systems.
• Strong experience designing for complex workflows, ideally in logistics, fleet, mobility, automotive, or enterprise platforms.
• Strong background in domain or enterprise architecture, solution architecture, or complex system design.
• Strong understanding of agentic AI concepts: agent skills, orchestration, grounding, memory, reasoning, tool use.
• Hands-on experience working with LLMs or agent frameworks in production contexts.
• Ability to collaborate deeply with data science on datasets, signals, evaluation, and learning loops.
• Comfort discussing trade-offs between deterministic logic, probabilistic reasoning, and learned behaviour.
• Experience integrating with customer systems (ERP, TMS, FSM, data platforms, event-driven architectures, etc.)
• Strong grasp of semantic models, schemas, and interface contracts.
• Comfortable working in fast-paced, cross-functional, and highly technical environments
• Experience collaborating with international and distributed teams across B2B/B2C context
• Fluency in spoken and written English.

• Innovative and modern technologies
• Truly international team of fantastic & talented people from 60+ countries worldwide, working from strong tech hubs located in Europe, US and Asia and multiple smaller locations
• A great work-life balance
• Challenging problems to solve
• Collaborative and encouraging colleagues
• Opportunities to learn, grow and develop
• Flexible working hours
• Competitive salary plus bonus
• Employee wellness programs and life-coaching sessions