Forward Deployed AI Engineer

Role overview

Accountabilities and responsibilities

• Work with stakeholders to understand the business need before building and take solutions from discovery to production.
• Design, build, deploy, and improve enterprise-grade agentic AI applications for aviation scenarios.
• Build agents that use tools and APIs, manage context, handle exceptions, and support human-in-the-loop workflows.
• Design RAG pipelines for enterprise knowledge, including ingestion, chunking, embeddings, vector search, retrieval tuning, grounding, and source traceability.
• Build MCP integrations and connect agents to backend systems through REST/OpenAPI, webhooks, and event-driven patterns.
• Implement secure authentication and expose clean, reusable, self-service interfaces.
• Apply tool calling, schema validation, retries, fallbacks, and guardrails to LLM applications.
• 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.
• Own a domain outcome, establish reusable patterns, and coordinate with product, architecture, and other squads.

Education and experience

• Around 3 years of production-grade software experience with GenAI and LLMs are requested.
• About 1 year of hands-on agentic AI experience is requested, covering tool calling, workflow orchestration, RAG, context management, evaluation, and monitoring.
• Hands-on experience or strong working knowledge of MCP is requested.
• Strong Python and basic TypeScript or JavaScript knowledge are requested.
• Experience integrating enterprise systems and deploying on cloud infrastructure with containers is requested.
• A bachelor’s degree in a relevant technical field or equivalent practical experience is required.
• Relevant cloud-AI, GenAI, agentic-AI, or MLOps certifications are advantageous.

Technical skills

• Modern engineering practice includes async programming, FastAPI, Pydantic, Git, CI/CD, testing, error handling, and logging.
• Experience with at least one agent framework or enterprise AI platform and one vector database or search platform is requested.
• Strong assets include aviation or airline knowledge, classical machine learning and data science, and classical full-stack development.

Working approach