Forward Deployed AI Engineer

About the Role
As a Forward Deployed AI Engineer, Technical Lead, you set the technical direction for a squad embedded in the business. This hands-on, build-first role requires you to sit with stakeholders to understand the problem and the reasons behind it, then design, build, deploy, and run enterprise-grade agentic AI systems that provide solutions. You will have single-threaded ownership of real aviation outcomes while collaborating with a Business Product Owner and an AI Value Architect on a shared platform that enables self-service against the company’s systems.

Accountabilities & Responsibilities

• Understand the business needs before building by engaging directly with stakeholders, eventually taking the solution from discovery to production.
• Design, build, deploy, and continuously improve enterprise-grade agentic AI applications addressing real aviation scenarios, utilizing agentic coding as your primary method of work.
• Build agents that can reason across steps, interact with tools and APIs, manage context, handle exceptions, and support human-in-the-loop processes reliably and at enterprise scale.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines over enterprise knowledge which includes ingestion, chunking, embeddings, vector search, retrieval tuning, grounding, and source traceability.
• Develop Model Context Protocol (MCP)-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks, and event-driven patterns with secure authentication, while exposing your work as clean, reusable, and self-serviceable interfaces.
• Apply structured LLM patterns from end to end including tool calling, schema-validated outputs, retries, fallbacks, and guardrails.
• Own quality from the outset by implementing testing, evaluation, observability, logging, versioning, and feedback loops to ensure reliability, accuracy, latency, security, and cost efficiency.
• Ensure security, privacy, access control, auditability, responsible AI, and governance are integral to every deployment.
• Take single-threaded ownership of a domain outcome to facilitate one owner and one result while helping to build reusable patterns that enhance the company’s internal AI capabilities.
• Coordinate with your Business Product Owner, AI Value Architect, and other squads and actively contribute to discussions when AI may not be the appropriate solution.
• Establish the technical direction and standards for the squad's agentic AI work, including making key architectural and build-versus-buy decisions.
• Design multi-agent and agent-to-agent systems, alongside evaluation frameworks that ensure their reliability, while leading delivery with external AI platforms and vendors as you build the company’s internal capabilities.
• Mentor and develop fellow engineers by conducting reviews, and promoting improvements on quality, security, and cost across the squad.

Education & Experience
We seek a technical lead who can set the engineering direction for agentic AI while remaining hands-on, combined with a business-first mindset:

• A profound curiosity; you delve into problems, challenge assumptions, and strive to comprehend how the airline operates.
• A business-first, human-centric mindset recognizing that aviation is designed for humans by humans, where AI is a supportive tool and not a replacement. Fluency in English is crucial and comfort within a culturally diverse, international team is essential.
• 8+ years of experience building production-grade software which includes at least 4 years with Generative AI, Large Language Models, and applied Machine Learning, alongside a minimum of 1 year of hands-on experience with agentic AI as an early adopter, demonstrating a history of setting technical direction and delivering agentic systems at scale.
• Practical knowledge of Model Context Protocol (MCP) for integrating agents to tools, systems, APIs, and data, alongside proficiency in Python and at least one of the following programming languages: TypeScript/JavaScript, Java, or C#. A strong understanding of modern engineering practices such as async programming, FastAPI, Pydantic, Git, CI/CD, testing, error handling, and logging.
• 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 familiarity with vector databases or search platforms (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch).
• Proven experience in integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud environments with containers, including monitoring and observability. Possess sound judgment regarding trade-offs among latency, quality, cost, reliability, security, privacy, responsible AI, and governance.
• Desired qualifications include domain knowledge in aviation or airlines, a background in classical machine learning and data science, and substantial experience in classical full-stack development (interfaces, frontends, APIs, backend engineering).
• A Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related technical field is preferred, alongside relevant cloud-AI, Generative AI, agentic-AI, or MLOps certifications.

Preferred For This Level

• Experience building AI agents for complex enterprise or operations-heavy workflows, including logistics, supply chain, aviation, cargo, customer operations, or contact centers.
• Familiarity 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 creating multi-agent systems, agent-to-agent communication patterns, agent registries, or tool-orchestration standards.
• Experience leading delivery alongside external AI platforms, startups, or vendors while enhancing internal engineering capabilities.

Growth Path
The natural next step from this position is the role of AI Value Architect, where you would own a cluster’s value journey while continuing to build alongside your team.