Forward Deployment Engineer

Xebia; Abu Dhabi; On-site; AED 350,000 - 520,000; Full time

Job summary

Xebia in the UAE is seeking a Forward Deployed AI Engineer to embed with aviation stakeholders, designing enterprise-grade agentic AI from concept to production.

You'll own end-to-end delivery, build scalable AI applications using MCP fabric, and collaborate with a Business Product Owner and AI Value Architect to maximize impact and ensure security and governance.

Qualifications

• Hands-on Core-level engineer with business-first mindset.
• Bachelor's degree in a technical field or equivalent practical experience.
• Experience with GenAI, LLMs, and agentic AI in production environments.

Responsibilities

• Understand before you build: start with business need and the why; take solution to production.
• Design, build, deploy and improve enterprise-grade agentic AI apps for aviation scenarios.
• Build agents that reason across steps, manage context, and support human-in-the-loop.
• Design RAG pipelines over enterprise knowledge: ingestion, embeddings, retrieval, grounding.
• Connect agents to backend systems via REST/OpenAPI with secure authentication.
• Apply structured LLM patterns end-to-end: tool calling, retries, guardrails.
• Own quality: testing, observability, logging, versioning and cost considerations.

Skills

Agentic AI

Python

LLMs

TypeScript/JS

APIs

Security & governance

Stakeholder collaboration

Education

Bachelor's degree in CS/Software Eng/Data Science/AI

Tools

MCP Fabric

LangGraph

Semantic Kernel

OpenAI Agents SDK

Azure OpenAI

Pinecone

Job description

At Xebia, we're building AI capability from the ground up. We don't buy off-the-shelf solutions. We design, build, deploy, and run AI systems ourselves.

As a Forward Deployed AI Engineer, you'll be embedded directly within the business, working closely with stakeholders to understand not just the problem, but the "why" behind it. You'll take ownership of real aviation outcomes, designing and delivering enterprise-grade Agentic AI solutions from concept to production.

Working alongside a Business Product Owner and an AI Value Architect, you'll leverage Xebia's AI platform, MCP fabric, and engineering standards to rapidly build scalable, secure, and impactful AI applications.

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.

Education & Experience

We look for a hands-on Core-level engineer who combines a business-first mindset with real agentic-AI engineering depth:

• 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.
• Around 3 years building production-grade software with GenAI and LLMs, including about 1 year of hands-on agentic AI: applications that go beyond prompting or basic chatbots, with tool calling, workflow orchestration, RAG, context management, evaluation and monitoring.
• Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs and data.
• Strong Python, with basic knowledge of at least one of TypeScript / JavaScript, 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.
• 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).
• Bachelor’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: grow into Senior Forward Deployed AI Engineer and Technical Lead, and onward to AI Value Architect, owning a cluster’s value journey while still building.