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Conversational Data Engineer

Acunor Inc

  • NULL, NJ
  • 5 days ago
  • Remote
  • $65–$70

Highlights

Work Model: Remote / Hybrid Role Overview We are seeking a hands-on Synthetic Data & RAG / Knowledge Pipeline Engineer to build high-quality synthetic datasets and scalable knowledge pipelines supporting conversational AI, voice, and chat solutions. This role focuses on creating realistic, privacy-safe data for AI configuration, testing, evaluation, and demonstrations, while also owning the ingestion, indexing, retrieval, and evaluation processes required for Retrieval-Augmented Generation (RAG) solutions.

Numbers & Facts

LocationNULL, NJ (
Remote)
Salary$65–$70

Description

Conversational Data Engineer
Experience: 8+ Years
Work Model: Remote / Hybrid
Role Overview
We are seeking a hands-on Synthetic Data & RAG / Knowledge Pipeline Engineer to build high-quality synthetic datasets and scalable knowledge pipelines supporting conversational AI, voice, and chat solutions.
This role focuses on creating realistic, privacy-safe data for AI configuration, testing, evaluation, and demonstrations, while also owning the ingestion, indexing, retrieval, and evaluation processes required for Retrieval-Augmented Generation (RAG) solutions.
The ideal candidate combines experience in data engineering, applied NLP, conversational AI data, synthetic data generation, and RAG/knowledge engineering.
Key Responsibilities
  • Analyze business domains to understand intents, entities, knowledge topics, document types, languages, conversational patterns, and edge cases.
  • Design and generate high-quality synthetic conversational datasets for voice and chat use cases.
  • Generate training and evaluation dialogues, customer/agent profiles, FAQs, knowledge-base articles, and structured entity data.
  • Build and maintain RAG and knowledge ingestion pipelines for enterprise conversational AI solutions.
  • Prepare, clean, transform, chunk, and organize documents and other knowledge sources for retrieval.
  • Load and manage data across appropriate cloud storage, data, search, and knowledge services.
  • Implement indexing and retrieval processes to ensure relevant information can be accurately retrieved by AI applications.
  • Evaluate retrieval quality, coverage, diversity, realism, and overall data quality.
  • Build parameterized and reusable data-generation and ingestion pipelines rather than customer-specific manual processes.
  • Implement and document knowledge-index refresh processes as source information changes.
  • Ensure synthetic datasets and source corpora meet privacy and data-isolation requirements, including validation for potential PII exposure.
  • Document generation approaches, parameters, assumptions, quality checks, and known limitations.
  • Collaborate with conversational AI, platform, and DevOps engineering teams to ensure datasets and knowledge indexes work correctly within deployed solutions.
Required Qualifications
  • 4+ years of experience in data engineering, applied NLP, conversational AI data, knowledge engineering, or related areas.
  • Hands-on experience building or supporting RAG / knowledge retrieval pipelines.
  • Understanding of document ingestion, preprocessing, chunking, indexing, retrieval, and evaluation.
  • Experience working with conversational datasets such as transcripts, FAQs, training dialogues, intents, entities, or knowledge articles.
  • Strong understanding of data quality, retrieval quality, and privacy/PII considerations.
  • Experience building automated, reusable data-processing or ingestion pipelines.
  • Strong analytical and problem-solving skills.
Preferred Qualifications
  • Hands-on experience with LLM-assisted synthetic data generation in production or enterprise implementation environments.
  • Experience generating synthetic conversations, evaluation datasets, knowledge articles, or similar AI datasets.
  • Familiarity with GCP and AWS data services, including technologies such as BigQuery, Amazon S3, and document/knowledge stores.
  • Experience with vector search, semantic retrieval, embeddings, or enterprise search technologies.
  • Experience evaluating RAG systems using retrieval and data-quality metrics.
  • Experience with multilingual synthetic-data generation or evaluation.
  • Familiarity with conversational AI, contact-center AI, voice bots, or enterprise chat applications.

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