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Open nowPosted 269 days ago

Data Engineer

Scrabble

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The clock on this job

Early applications get read.

11.9% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 269 days ago

The posting

Data Engineer - Sampling JIO

Company Overview

(Company information is not specified.)

Job Summary

The Data Engineer will be responsible for designing and implementing core data pipelines and semantic data models for the AI Platform. This role is crucial for enabling high-quality data retrieval, grounding, and minimizing hallucination effects in multi-domain Large Language Model (LLM) applications.

Responsibilities

  • Design and implement core data pipelines and semantic data models.
  • Ensure high-quality data retrieval and grounding for AI applications.
  • Minimize hallucination in multi-domain LLM applications.
  • Collaborate with data science teams to enhance data-driven functionalities.
  • Optimize and maintain existing data pipelines for performance and reliability.

Qualifications

  • 5+ years of experience in data engineering or data science.
  • Strong proficiency in Python and SQL.
  • Experience with ETL/ELT systems.
  • Hands-on experience with GraphQL schema design.
  • Familiarity with at least one vector database (e.g., Pinecone, Milvus, Weaviate, FAISS).
  • Solid understanding of concepts such as embeddings, chunking, metadata enrichment, and retrieval pipelines.
  • Experience with Airflow, Spark, Flink, or similar data processing systems.
  • Bachelor's degree in Computer Science, Data Science, or a related field is preferred.

Preferred Skills

  • Familiarity with machine learning frameworks and tools.
  • Experience in cloud platforms such as AWS, Google Cloud, or Azure.
  • Knowledge of data governance and best practices in data management.

Experience

- Minimum of 5 years in data engineering or data science roles.

Environment

(Work environment details are not specified.)

Salary

(Salary details are not specified.)

Growth Opportunities

(Career advancement opportunities are not specified.)

Benefits

(Benefits information is not specified.)

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