Senior Data Engineer (AI/ML)

This role is 100% remote across India location About OpenTable With millions of diners, 70,000+ restaurant partners and 25+ years of experience, OpenTable, part of Booking Holdings, Inc. (NASDAQ: BKNG), is an industry leader with a passion for helping restaurants thrive. Our world-class technology empowers restaurants to focus on what matters most – their team, their guests, and their bottom line – while enabling diners to discover and book the perfect restaurant for every occasion.

Every employee at OpenTable has a tangible impact on what we do and how we do it. You’ll also be part of a global team and its portfolio of metasearch brands. Hospitality is all about taking care of others, and it defines our culture.

About Role We are looking for a Senior Data Engineer – AI/ML to help build the data and AI infrastructure powering our next generation of intelligent products and experiences.

This role combines modern data engineering with Generative AI. You will design scalable data platforms and pipelines while building production-grade solutions using LLMs, RAG, embeddings, vector search, and AI agents. You will work closely with data scientists, ML engineers, software engineers, and product teams to turn AI capabilities into reliable, scalable production systems.

What You’ll Do

• Design and build AI/LLM data pipelines supporting training, inference, evaluation, embeddings, and retrieval workloads.
• Build production-grade RAG systems, including ingestion, chunking, embedding generation, indexing, retrieval, reranking, and context construction.
• Develop AI applications using LLMs, structured outputs, function/tool calling, and agentic workflows.
• Build and optimize semantic search and vector retrieval systems.
• Develop frameworks for LLM evaluation, monitoring, tracing, quality measurement, latency, and cost optimization.
• Design scalable batch and streaming pipelines using Databricks, Apache Spark, Delta Lake, Snowflake, and Airflow.
• Build data products and platforms that make structured and unstructured enterprise data accessible to AI applications.
• Develop reliable ETL/ELT pipelines and optimize large-scale distributed workloads for performance and cost.
• Establish data quality, governance, lineage, security, and observability practices.
• Partner with ML and application engineering teams to move AI prototypes into production-ready systems.

Required Qualifications

• 5+ years of experience in data engineering, software engineering, distributed systems, or a related field.
• Strong programming skills in Python and/or Scala/Java and advanced SQL.
• Hands-on experience with Databricks, Snowflake, Apache Spark, Delta Lake, and Airflow.
• Strong experience with cloud data platforms such as Snowflake and/or Databricks.
• Practical experience building applications using LLMs or Generative AI.
• Strong understanding of RAG architectures, embeddings, vector databases, semantic search, and retrieval systems.
• Familiarity with LLM concepts including prompting, structured outputs, tool calling, and model evaluation.
• Experience designing scalable, reliable, and observable production data systems.

Preferred Qualifications

• Deep experience designing large-scale data platforms, distributed processing systems, and complex data workflows.
• Strong experience with real-time and streaming data architectures, including Kafka, Spark Structured Streaming, or similar technologies.
• Experience building low-latency data pipelines and event-driven architectures.
• Experience designing complex multi-stage ETL/ELT and data orchestration workflows using Airflow or similar platforms.
• Experience optimizing Spark/Databricks workloads, including partitioning, clustering, caching, joins, and compute optimization.
• Experience building data platforms supporting both batch and real-time AI/ML workloads.
• Experience with LLM and AI evaluation frameworks, including automated evaluations, offline/online evaluation, quality metrics, and experimentation.
• Experience building evaluation datasets and pipelines for measuring LLM/RAG/agent quality, accuracy, relevance, latency, and cost.
• Experience with AI observability and tracing, including token usage, model performance, latency, failures, and production monitoring.
• Experience with LangGraph, LangChain, LlamaIndex, or similar AI orchestration frameworks.
• Experience with vector databases such as Qdrant, Pinecone, Weaviate, or Databri…