Forward Deployment Engineer
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Key skills for this role
Role Overview
The Forward Deployed AI Engineer works directly with business stakeholders to understand problems and deliver enterprise-grade agentic AI solutions.
The role focuses on real aviation outcomes and works with a Business Product Owner and AI Value Architect.
The engineer uses Xebia’s AI platform, MCP fabric and engineering standards to build scalable, secure and impactful applications.
Key Skills for This Role
Full Job Posting
About Xebia
Xebia is building AI capability internally by designing, building, deploying and operating its own AI systems.
Role Overview
The Forward Deployed AI Engineer works directly with business stakeholders to understand problems and deliver enterprise-grade agentic AI solutions.
The role focuses on real aviation outcomes and works with a Business Product Owner and AI Value Architect.
The engineer uses Xebia’s AI platform, MCP fabric and engineering standards to build scalable, secure and impactful applications.
Accountabilities and Delivery
- Design, build, deploy and improve enterprise-grade agentic AI applications for aviation scenarios.
- Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop workflows.
- Take solutions from discovery to production and own a domain outcome.
- Coordinate with product, architecture and engineering stakeholders, and identify when AI is not the right tool.
AI Engineering
- Implement RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.
- Build MCP integrations with backend systems through REST/OpenAPI, webhooks and event-driven patterns.
- Apply tool calling, schema-validated outputs, retries, fallbacks and guardrails.
- Own 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 deployments.
Education and Experience
- Around three years building production-grade software with GenAI and LLMs, including about one year of hands-on agentic AI experience.
- Hands-on experience or strong working knowledge of MCP.
- Strong Python and basic knowledge of at least one of TypeScript or JavaScript.
- Knowledge of async programming, FastAPI, Pydantic, Git, CI/CD, testing, error handling and logging.
- Experience with an agent framework or enterprise AI platform and a vector database or search platform.
- Experience integrating enterprise systems and deploying on cloud with containers.
- Bachelor’s degree in a related technical field or equivalent practical experience.
- Fluent English and comfort in a culturally diverse international team.
Advantageous Background
- Aviation or airline domain knowledge is a strong asset.
- Classical machine learning, data science and full-stack development experience are strong assets.
- Relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are advantageous.
Growth Path
The role can progress to Senior Forward Deployed AI Engineer, Technical Lead and AI Value Architect.
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