JOB SUMMARY
This is a hands-on Individual Contributor (IC) role focused on designing, developing, modernizing, and supporting enterprise-scale notification services and AI-enabled communication platforms used across customer and crew applications. The ideal candidate will be an experienced Java Backend Engineer with strong AWS Cloud, Microservices, and AI Engineering expertise, including LLM integration, RAG pipelines, vector databases, tool calling, and cloud-native deployments. This role requires someone who spends the majority of their time coding, debugging, designing, and delivering production-ready software.
Key Responsibilities
Design and develop enterprise notification platform services and APIs.
Build scalable Java Spring Boot microservices for customer and crew communication platforms.
Develop AI-powered notification capabilities using Large Language Models (LLMs).
Design and implement: RAG pipelines, Embeddings & semantic search, Vector database integrations, Prompt engineering, AI-assisted communication workflows.
Create JSON tool schemas enabling AI agents to interact with enterprise systems.
Build cloud-native applications using AWS services.
Deploy applications using Docker, Kubernetes, and CI/CD pipelines.
Develop event-driven integrations using Apache Camel and messaging technologies.
Optimize production systems for scalability, resiliency, performance, and security.
Troubleshoot production issues and support enterprise-scale applications.
Participate in architecture discussions, code reviews, documentation, testing, and release management.
Required Qualifications
8+ years of software engineering experience.
8+ years of hands-on Java backend development.
Strong experience with: Spring Boot, REST APIs, Microservices.
4+ years of AWS cloud development.
3+ years working with event-driven architectures and messaging systems.
Experience with Apache Camel or similar integration frameworks.
Strong production experience implementing AI/GenAI solutions including: LLM integration, Prompt Engineering, RAG, Embeddings, Vector Search, Semantic Search, AI Workflow Automation.
Experience with Docker, Kubernetes, and CI/CD.
Knowledge of secure AI engineering, AI guardrails, data privacy, and AI governance.
Experience with monitoring tools such as Dynatrace, ELK Stack, or CloudWatch.
Strong understanding of SDLC, Git, automated testing, release management, and production support.
Preferred Qualifications
Experience with enterprise notification or messaging platforms.
Twilio or similar communication platform experience.
AI-powered personalization and content generation.
AI observability, LLMOps, or MLOps.
AWS Certification.
Airline industry experience.
