Senior Data Engineer (AI/ML)
New
O
OpenTableHospitality Technology
This role is 100% remote across India location, Global workforce operating across multiple time zones; expect to manage communications outside regular working hours.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLJavaSnowflakeAirflowSparkScalaDatabricksGenerative AI
Requirements
- 5+ years of experience in data engineering, software engineering, distributed systems, or a related field.
- Strong programming skills in Python and/or Scala/Java.
- Advanced SQL skills.
- 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.
Responsibilities
- 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.
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