Engineer - Generative AI
United Airlines
Location
🇮🇳 Gurugram, India
Type
full_time
Salary
Undisclosed
Posted
1w ago
Job Description
Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness
benefits
, build lasting connections with your team and our customers, and travel the world using our extensive route network. Come join us to create what’s next. Let’s define tomorrow, together. Description United's Digital Technology team is comprised of many talented individuals all working together with cutting-edge technology to build the best airline in the history of aviation. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.
Job overview
and
responsibilities
We are seeking a Generative AI Engineer to build and scale AI-native platforms and services. This role requires a strong full-stack engineering foundation combined with hands-on experience in Generative AI, cloud-native architectures, and MLOps. You will contribute to the design and development of intelligent systems, AI agents, and reusable platforms, working across backend, frontend, and middleware layers. The ideal candidate brings both hands-on delivery capability and architectural awareness to build robust, scalable, and observable AI solutions. • Design, develop, and deploy AI-native applications and services leveraging Generative AI and LLMs • Build and maintain end-to-end solutions across backend (Python), middleware, and frontend layers • Develop scalable APIs and microservices to enable AI-driven capabilities across platforms • Implement and operationalize LLM-based workflows, including prompt orchestration, RAG pipelines, and agent frameworks • Contribute to architecture design and system decomposition, ensuring scalability, resilience, and extensibility • Build and manage cloud-native solutions on AWS, leveraging services such as Lambda, ECS/EKS, S3, and API Gateway • Establish and maintain MLOps practices, including CI/CD pipelines, model deployment, versioning, and monitoring • Implement observability and telemetry frameworks (logging, tracing, metrics) to ensure reliability and performance of AI systems • Collaborate with cross-functional teams to translate business