Required Machine Learning Engineer II - GenAI applications
ID 30497
This opening is for the GenAI Applications Team within the Data & AI Marketplace department.
The GenAI Applications team is responsible for designing and delivering agentic, ML-powered solutions for some of our most impactful products, including booking search experiences, trip planning, and trip helpfulness. The team builds AI-driven applications and conversational agents, such as chatbots and intelligent assistants, that significantly enhance the end-to-end customer experience.
Role Description:
In this role, you will design, build, and operate production-grade AI/machine learning applications for GenAI products. You will integrate foundation models with data, services, and tools; build agent workflows and own the systems end to end, including their reliability, observability, tracing, and quality. You will work closely with software engineers, ML scientists, and product teams to deliver scalable applications for travelers.
Key Job Responsibilities and Duties:
Design, build, and productionize AI and agent-based systems by integrating LLMs with data, services, tools, and ML models. Develop the workflows needed to deliver complete product experiences, ensuring they are scalable, efficient, reliable, and accurate.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Build and maintain production-grade ML applications and tooling, including service and API integrations, testing, deployment, and CI/CD. Work with software engineers and DevOps to ensure reliable inference and maintainable operation in production.
Own ML applications and services end to end. Instrument them with metrics, logs, and traces; monitor application health, performance, reliability, and model output quality; and investigate issues and drive improvements when targets are not met.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.