Senior Machine Learning Engineer (gen AI & Multi-agentic Systems)

Expedia Expedia · Hospitality · Bangalore, India

Senior Machine Learning Engineer at Expedia Group focused on building end-to-end GenAI and multi-agent systems for consumer travel applications. The role involves architecting, developing, and deploying production-ready multimodal LLMs, RAG pipelines, agentic ecosystems, and integrating them with user interfaces and observability features. Responsibilities include LLM training/adaptation, memory architectures, distributed training/inference, and influencing product roadmaps.

What you'd actually do

  1. Architect, build, and ship enterprise-scale GenAI, RAG, and multi-agent systems end-to-end, including frontend, backend, and user interfaces.
  2. Design hierarchical multi-agent ecosystems with interactive UIs, dashboards, and safety/observability features.
  3. Develop memory architectures: short-term contextual memory, long-term episodic memory, knowledge graph augmentation, and adaptive retrieval systems.
  4. Lead hands-on implementation of RAG pipelines, vector memory systems, and agent orchestration frameworks (LangChain, AutoGen, OpenAI Agents SDK).
  5. Train, fine-tune, adapt (LoRA/QLoRA/adapters), and distill LLMs, including RLHF/DPO, for production-ready chatbots and GenAI products.

Skills

Required

  • software engineering
  • ML
  • AI systems
  • production GenAI deployments
  • LLM training
  • LLM adaptation
  • LLM distillation
  • RLHF/DPO
  • RAG systems
  • multi-agent AI platforms
  • observability frameworks
  • safety frameworks
  • distributed GPU training
  • distributed GPU inference
  • cloud infrastructure (AWS/Azure)
  • container orchestration
  • ML tooling
  • end-to-end product development
  • UX/UI design
  • frontend integration
  • backend integration
  • communication skills

Nice to have

  • PhD in Computer Science, Machine Learning, or a related field
  • publications
  • patents
  • talks
  • open-source contributions
  • multimodal LLM systems (vision, audio, music, structured data)
  • GenAI safety
  • evaluation
  • testing
  • monitoring
  • modeling
  • infrastructure
  • product
  • design

What the JD emphasized

  • building end-to-end systems
  • backend architecture
  • user-facing interfaces
  • UX design
  • observability
  • product integration
  • production GenAI deployments
  • LLM training, adaptation, distillation, RLHF/DPO, and RAG systems
  • building and operating multi-agent AI platforms
  • distributed GPU training and inference
  • lead end-to-end product development

Other signals

  • building end-to-end systems
  • production GenAI deployments
  • multi-agent AI platforms
  • distributed GPU training and inference