Key Responsibilities:
Design and Develop:
- Architect and build an agentic LLM platform capable of ingesting and reasoning over structured and unstructured data sources (real-time flight operations feeds, regulatory documents, SOPs).
- Implement multi-agent orchestration for complex decision-support scenarios.
Data Integration:
- Develop pipelines for real-time data ingestion from flight operations systems (e.g., AODB, FIDS, ATC feeds).
- Incorporate static content such as ICAO/IATA regulations, airport operating procedures, and safety manuals.
AI/ML Engineering:
- Fine-tune large language models for domain-specific tasks (aviation operations, compliance).
- Implement retrieval-augmented generation (RAG) and knowledge graph integration for accurate responses.
System Reliability & Compliance:
- Ensure platform meets aviation safety and regulatory standards.
- Build robust monitoring and fallback mechanisms for mission-critical decision support.
Collaboration:
- Work closely with airport operations teams, solution architects, and data engineers to align technical solutions with operational needs.
Required Skills & Experience:
Technical Expertise:
- Strong experience with LLMs (OpenAI, HuggingFace, Azure OpenAI) and agentic frameworks (LangChain, AutoGen, CrewAI).
- Proficiency in Python and cloud platforms (Azure preferred).
- Experience with real-time data streaming (Kafka, Event Hub) and API integrations.
Domain Knowledge:
- Familiarity with aviation operations, airport systems, and regulatory frameworks (ICAO, IATA).
- Understanding of decision-support systems and operational resilience.
Other:
- Excellent problem-solving skills and ability to work in high-stakes environments.
- Strong communication and stakeholder engagement skills.
