RE : Openings for Data Engineer – AI Implementation - Contract to Hire Position - Remote

Role: Data Engineer – AI Implementation

Experience: Relevant experience in Data Engineering and AI data preparation

Technical Skills: Python, SQL, ETL/ELT, RAG, Qdrant, Neo4j, MongoDB

Role Overview

We are looking for a Data Engineer to support enterprise AI implementations by preparing, transforming, validating, and managing data for AI agents, automated workflows, and Retrieval-Augmented Generation (RAG) knowledge systems.

The candidate will work closely with Product Managers, AI Implementation Engineers, and client teams to ensure that structured data, semi-structured data, and enterprise documents are accurate, accessible, and ready for AI applications.

Key Responsibilities

1. Data Engineering & AI Workflows

• Define data requirements, schemas, field mappings, and transformation rules.
• Build and maintain ETL/ELT pipelines for data cleaning, transformation, and validation.
• Process structured and semi-structured data from APIs, databases, files, and integration sources.
• Identify and resolve missing values, duplicate records, inconsistent formats, and data quality issues.
• Prepare test datasets and validate data outputs for AI agents and automated workflows.

2. Knowledge Systems & RAG

• Extract, parse, and prepare enterprise documents for ingestion into AI knowledge bases.
• Preserve document structure, tables, metadata, and source references during processing.
• Implement document chunking, metadata extraction, and content versioning for RAG applications.
• Work with vector databases such as Qdrant to support embeddings, indexing, similarity search, and metadata filtering.
• Build and maintain knowledge graphs using Neo4j, data modelling, entity relationships, and Cypher queries.
• Manage document ingestion, updates, deletions, and troubleshooting of missing or outdated content.
• Apply OCR techniques when processing scanned documents.

3. Data Operations, Security & Governance

• Monitor pipeline performance, data quality, and content freshness.
• Implement error handling, retries, reconciliation checks, and recovery mechanisms.
• Follow data privacy, access control, credential management, retention, and deletion requirements.
• Maintain data lineage, version-controlled pipeline code, technical documentation, and operational procedures.
• Support release testing, production troubleshooting, and operational handover.

Required Skills & Qualifications

• Experience in data engineering or data-intensive software implementations.
• Strong proficiency in Python and SQL.
• Hands-on experience building ETL/ELT pipelines and data processing workflows.
• Understanding of APIs, JSON, databases, schemas, data mapping, and file-based data exchange.
• Experience with data cleansing, transformation, validation, and quality management.
• Practical knowledge of RAG data preparation, document parsing, chunking, and metadata management.
• Experience with vector databases, preferably Qdrant, including embeddings and similarity search.
• Knowledge of Neo4j, knowledge graphs, data modelling, and Cypher query language.
• Familiarity with MongoDB and structured or semi-structured data.
• Understanding of pipeline monitoring, error handling, version control, data lineage, and data governance.
• Strong analytical, troubleshooting, communication, and collaboration skills.

Technology Stack

• Programming & Querying: Python, SQL
• Databases: MongoDB
• Vector Database: Qdrant
• Knowledge Graph: Neo4j, Cypher
• AI & Knowledge Systems: RAG, embeddings, document chunking, metadata extraction, semantic search
• Data Engineering: ETL/ELT, data pipelines, API integration, data validation, OCR
• Additional Technologies: Node.js, React.js

Interested can share resume to this Mail ID - [Confidential Information]