Forward Deployment Architect

Role purpose

Accountabilities and responsibilities

• 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.
• Implement RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval tuning, grounding, and source traceability.
• Build MCP integrations with REST/OpenAPI, webhooks, event-driven patterns, secure authentication, and reusable interfaces.
• Apply tool calling, schema validation, retries, fallbacks, and guardrails to LLM solutions.
• 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.
• Set squad technical direction, architecture standards, and build-versus-buy decisions.
• Design multi-agent systems and evaluation frameworks, lead external platform delivery, and build internal capability.
• Mentor engineers, review work, and improve quality, security, and cost across the squad.

Education and experience

• 8+ years building production-grade software, including 4+ years with GenAI, LLMs, and applied machine learning.
• At least 1 year of hands-on agentic AI experience with a record of setting technical direction and shipping systems at scale.
• Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs, and data.
• Strong Python plus strong knowledge of at least one of TypeScript, JavaScript, Java, or C#.
• Experience with modern engineering practices, an agent framework or enterprise AI platform, and a vector database or search platform.
• Experience integrating enterprise systems and deploying cloud applications with containers, monitoring, and observability.
• Fluent English and comfort working in a culturally diverse international team.

Preferred qualifications

• Aviation or airline domain knowledge, classical machine learning and data science, and classical full-stack development are strong assets.
• Experience with enterprise operations workflows, voice AI, automation, agent evaluation, multi-agent systems, or external AI vendors is preferred.
• A relevant master's degree or practical experience is acceptable; relevant cloud-AI, GenAI, agentic-AI, or MLOps certifications are advantageous.