Forward Deployment Architect

Job Purpose Xebia is building in-house AI expertise to deliver AI in aviation. We build; we do not buy off the shelf. As a Forward Deployed AI Engineer, Technical Lead you set the technical direction for a squad embedded in the business: you sit with stakeholders to understand the problem and the why, then design, build, deploy and run the agentic AI systems that solve it, end to end. This is a hands-on, build-first role with single-threaded ownership of a real aviation outcome, working alongside a Business Product Owner and an AI Value Architect, on a shared platform (paved road, MCP fabric, standards) that lets your squad self-serve against Customer’s systems. Accountabilities & Responsibilities Understand before you build. Start every problem with the business need and the why, working directly with stakeholders, then take the solution from discovery to production. • Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working. • Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale. • Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability. • Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces. • Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails. • Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost. • Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment. • Take single-threaded ownership of a domain outcome (one owner, one result) and help establish reusable patterns that grow Customer’s internal AI capability rather than renting it. • Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool. • Set the technical direction and standards for the squad’s agentic AI work, and make the key architecture and build-vs-buy calls. • Design multi-agent and agent-to-agent systems and evaluation frameworks that keep them reliable, and lead delivery with external AI platforms and vendors while building Customer internal capability. • Grow the engineers around you: mentor, review, and raise the bar on quality, security and cost across the squad. Education & Experience We look for a technical lead who sets the engineering direction for agentic AI while still building, and combines that with a business-first mindset: • Curiosity above all: you dig into problems, question assumptions and want to understand how the airline actually works. • A business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team. • 8+ years building production-grade software, including 4+ years with GenAI, LLMs and applied ML and at least 1 year of hands-on agentic AI as an early adopter, with a track record of setting technical direction and shipping agentic systems at scale. • Hands-on experience or strong working knowledge of MCP (Model Context Protocol) for connecting agents to tools, systems, APIs and data. • Strong Python, with strong knowledge of at least one of TypeScript / JavaScript, Java, or C#, and modern engineering practice: async programming, FastAPI, Pydantic, Git and CI/CD, testing, error handling and logging. • Practical experience with at least one agent framework or enterprise AI platform (e.g. LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, Google Vertex/Gemini) and with a vector database or search platform (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch). • Experience integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud with containers, monitoring and observability; sound judgement on the trade-offs of latency, quality, cost and reliability, and on security, privacy, responsible AI and governance. • Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering). • Preferred for this level: • Experience building AI agents for complex enterprise or operations-heavy workflows (logistics, supply chain, aviation, cargo, customer operations or contact centre). • Experience with voice AI, email automation, CRM integrations, workflow automation or multilingual agents. • Experience designing golden test sets, simulation-based testing, regression testing and agent evaluation frameworks. • Experience designing multi-agent systems and agent-to-agent communication patterns, agent registries or tool-orchestration standards. • Experience leading delivery with external AI platforms, startups or vendors while building internal engineering capability. • Master’s degree in computer science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage. • Growth path: the natural next step is AI Value Architect, owning a cluster’s value journey while still building alongside the team. 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8+ years building production-grade software, including 4+ years with GenAI, LLMs and applied ML and at least 1 year of hands-on agentic AI as an early adopter, with a track record of setting technical direction and shipping agentic systems at scale. Hands-on experience or strong working knowledge of MCP (Model Context Protocol) for connecting agents to tools, systems, APIs and data. Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering). Master’s degree in computer science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.

Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working. Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale. Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability. Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces. Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails. Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost. Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment. Take single-threaded ownership of a domain outcome (one owner, one result) and help establish reusable patterns that grow Customer’s internal AI capability rather than renting it. Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool. Set the technical direction and standards for the squad’s agentic AI work, and make the key architecture and build-vs-buy calls. Design multi-agent and agent-to-agent systems and evaluation frameworks that keep them reliable, and lead delivery with external AI platforms and vendors while building Customer internal capability. Grow the engineers around you: mentor, review, and raise the bar on quality, security and cost across the squad.