Forward Deployed AI Engineer

Role overview

Accountabilities and responsibilities

• Discover business needs with stakeholders and take solutions from discovery to production.
• Build and improve enterprise agentic AI applications for aviation scenarios.
• Develop agents that reason across steps, call tools and APIs, manage context, handle exceptions, and support human-in-the-loop work.
• Build RAG pipelines covering knowledge ingestion, chunking, embeddings, vector search, retrieval tuning, grounding, and traceability.
• Integrate agents with backend systems through MCP, APIs, webhooks, event-driven patterns, and secure authentication.
• Use structured LLM patterns such as tool calling, schema-validated outputs, retries, fallbacks, and guardrails.
• Own testing, evaluation, observability, logging, versioning, and feedback loops for quality, reliability, latency, security, and cost.
• Apply security, privacy, access control, auditability, responsible AI, and governance.
• Own a domain outcome and develop reusable patterns that strengthen internal AI capability.
• Coordinate with product and value partners and other squads; assess whether AI is suitable for a problem.
• Set squad technical standards and direction, make architecture and build-versus-buy decisions, and design multi-agent systems and evaluation frameworks.
• Lead vendor and platform delivery, mentor engineers, review work, and raise quality, security, and cost standards.

Experience and background

• 8+ years building production-grade software, including 4+ years with GenAI, LLMs, and applied ML.
• At least 1 year of hands-on agentic AI experience, with a record of setting direction and shipping agentic systems at scale.
• Hands-on experience or strong working knowledge of MCP.
• Strong Python and strong knowledge of at least one of TypeScript, JavaScript, Java, or C#.
• Modern engineering practice includes asynchronous programming, testing, error handling, and logging; FastAPI, Pydantic, Git, and CI/CD are cited.
• Practical experience with at least one agent framework or enterprise AI platform and a vector database or search platform.
• Experience integrating enterprise systems and deploying cloud workloads with containers, monitoring, and observability.
• Sound judgment concerning latency, quality, cost, reliability, security, privacy, responsible AI, and governance.
• Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related technical field, or equivalent practical experience.
• Fluent English; comfortable in a culturally diverse international team, with a business-first and human-centric mindset.

Preferred experience and assets

• Aviation or airline knowledge, classical machine learning and data science, and full-stack development are strong assets.
• Preferred experience includes complex enterprise or operations-heavy workflows, such as logistics, supply chain, aviation, cargo, customer operations, or contact centres.
• Voice AI, email automation, CRM integrations, workflow automation, and multilingual agents are preferred experience.
• Golden test sets, simulation-based and regression testing, agent evaluation frameworks, multi-agent systems, agent-to-agent communication, registries, and orchestration standards are preferred experience.
• Experience leading external AI platform, startup, or vendor delivery while building internal engineering capability is preferred.
• Relevant cloud-AI, GenAI, agentic-AI, or MLOps certifications are an advantage.

Growth path