Software Engineer (AI / Full Stack)

About Us

TrustFlight is an innovative aviation software company that specializes in developing cutting-edge AI, digital workflow, and analytics applications for the aviation industry. Our software empowers many of the world’s airlines, business jet operators, MROs, training organizations, and aviation service providers to enhance safety, streamline operations, and improve overall efficiency.

We are one of the largest independent software providers in aviation, trusted by more than 1,600 organizations in over 120 countries, including many of the world’s top operators.

Why Choose TrustFlight?

✈️ Our Mission: To revolutionize aviation by delivering digital workflow solutions that enhance safety, streamline operations, and inspire confidence across the industry.

🚀 Impact: Over 200,000 users rely on our systems every day for operational safety, compliance management, and mission-critical decision-making.

🚩 Core Values: Guided by integrity, responsibility, innovation, and excellence, we are committed to empowering our partners to operate with confidence.

Join us in shaping the future of aviation and making an impact through technology.

The Role

You will join TrustFlight as a Software Engineer embedded in a product squad delivering AI-powered capabilities for safety- and compliance-critical aviation workflows, reporting to the Tech Lead. This is a full-stack role with a strong emphasis on AI delivery. You will ship end-to-end AI features across web applications, backend services, and a Python-based ingestion and processing pipeline. You will also serve as a liaison with the central AI team, applying shared patterns and integrating future improvements (orchestration, observability, evaluation) into production.

What you’ll be doing

• Own AI reliability and iteration by delivering production-grade AI features that are measurable, debuggable, and safe for compliance-critical workflows.

• Maintain and improve existing AI-powered workflows that drive customer trust and product adoption.

• Implement and evolve agent-style capabilities using the internal agent framework (tool calling, streaming, multi-agent patterns where appropriate).

• Integrate LLM providers used in production:

  • OpenAI

  • Anthropic

  • Google GenAI

• Build tools that connect models to product context, such as:

  • Web search

  • Embeddings-based document querying (retrieval)

  • Document outline/markdown/content extraction for grounding

  • AI-assisted change proposal workflows

• Harden AI behaviour and safety with guardrails, prompt/version management, and privacy-aware handling of customer data.

• Raise quality with automated testing and contribute to end-to-end coverage for critical workflows.