Software Development Engineer III (ai)

Expedia Expedia · Hospitality · Gurgaon, India, India

Software Development Engineer III for EG Finance AI platform engineering team, responsible for end-to-end technical execution of AI products, from solution design through production scale. Requires expertise in ML engineering, LLMs, software systems, and AI platforms, focusing on building reusable, scalable AI capabilities. Will work closely with Product Managers and Engineers to deliver AI systems that are trusted, explainable, and production ready. Requires deep hands-on expertise across software engineering, ML, LLMs, and MLOps.

What you'd actually do

  1. Design and build end-to-end AI products and deployment pipelines.
  2. Own technical architecture for AI workflows including prompt engineering, RAG, and agentic systems
  3. Develop and review backend services, APIs, and data pipelines powering AI products
  4. Translate complex Finance problems into scalable AI system designs
  5. Ensure AI solutions meet standards for performance, reliability, security, and governance

Skills

Required

  • Python
  • SQL
  • 5+ years of development experience in an enterprise-level engineering environment
  • Proven experience building and deploying ML / AI / LLM-powered systems in production
  • Deep understanding of prompt engineering, RAG architectures, and agentic workflows
  • Experience with modern LLMs and frameworks such as OpenAI, Claude, Gemini, Llama, LangChain, Langflow, LlamaIndex, Semantic Kernel
  • Strong software engineering background: API design, distributed systems, system integration
  • Experience with data platforms, semantic modeling, and time-series logic
  • Good knowledge of Data Structures and Algorithm.
  • Solid understanding of cloud-native architectures, containers, and CI/CD pipelines
  • Experience with MLOps concepts such as monitoring, evaluation, and lifecycle management
  • Understanding of AI security, auditability, and compliance considerations

Nice to have

  • Java

What the JD emphasized

  • deep hands-on expertise across ML engineering, LLMs, software systems, and AI platforms
  • deep hands-on expertise across software engineering, ML, LLMs, and MLOps
  • Proven experience building and deploying ML / AI / LLM-powered systems in production
  • Deep understanding of prompt engineering, RAG architectures, and agentic workflows

Other signals

  • end-to-end AI products
  • production scale
  • ML engineering
  • LLMs
  • AI platforms
  • reusable, scalable AI capabilities
  • trusted, explainable, and production ready AI systems
  • MLOps
  • prompt engineering
  • RAG
  • agentic systems
  • monitoring, logging, evaluation, and cost control
  • best practices for AI development, deployment, and maintenance