RL
AI Data Engineer
Resource Logistics, Inc.
🇺🇸 United States
Hybrid
Mid level
2 months ago
- AI
- Python
- AI/ML
- Machine Learning
- ETL
- ELT
- Large Language Models
- RAG
- SQL
- PySpark
- Data Modeling
- Snowflake
- Apache Spark
- Databricks
- Airflow
- Dataflow
- Informatica
- NoSQL
- REST API
- Microservices
- Git
- CI/CD
- Devops
- Pinecone
- Chroma
- FAISS
- OpenAI
- Gemini
- Claude
2 months ago
Role: AI Data Engineer
Mandatory Skills- Python, AI/ML
Retail domain experience is highly preferable.
JD:
We are seeking an experiencedAI Data Engineer (15+ Years) to design, develop, and manage scalable data platforms that enable advanced analytics, Machine Learning (ML), and Generative AI solutions. The ideal candidate will build robust data pipelines, ensure data quality, and integrate AI/ML capabilities into enterprise data ecosystems.
Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
- Build and optimize data lakes, data warehouses, and AI-ready data platforms.
- Develop ingestion, transformation, and orchestration frameworks using cloud-native technologies.
- Prepare, cleanse, and engineer datasets for AI/ML and Generative AI workloads.
- Integrate Large Language Models (LLMs), vector databases, embeddings, and RAG (Retrieval-Augmented Generation) pipelines into enterprise solutions.
- Implement data governance, security, lineage, and quality controls.
- Collaborate with Data Scientists, AI Engineers, Business Analysts, and Solution Clienthitects.
- Monitor, troubleshoot, and optimize data pipelines and platform performance.
- Automate deployment, testing, and monitoring of data engineering workflows.
- Create technical documentation and data dictionaries for enterprise data assets.
Technical Skills
- Python, SQL, PySpark
- ETL/ELT development
- Data Modeling (Star Schema, Snowflake Schema)
- Apache Spark, Databricks
- Airflow, Dataflow, Informatica, ADF, Synapse, or equivalent tools
- Relational & NoSQL Databases
- Data Warehousing concepts
- REST APIs and Microservices
- Git, CI/CD, DevOps practices
AI & GenAI Skills
- Machine Learning fundamentals
- Data preparation for AI models
- Vector Databases (Pinecone, ChromaDB, FAISS)
- LLM Integration (OpenAI, Clienture OpenAI, Gemini, Claude, etc.)
- RAG Clienthitecture
- Embeddings and Semantic SeClienth
- Prompt Engineering fundamentals
AI Data Engineer · Resource Logistics, Inc.