AI Data Engineer

Siri InfoSolutions Inc

New York, NY, United States

5 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Compensation

$92,300.0 - $166,850.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Artificial Intelligence Amazon Web Services Business Analytics Applications Applications Architecture Microsoft Azure Cloud Engineering Databases Continuous Integration Data Architecture Information Engineering Data Infrastructure Data Integration

+17 more

Extract Transform Load (ETL) Data Warehousing Python (Programming Language) Software Deployment Software Engineering Systems Integration Enterprise Data Management Data Processing Large Language Models Git Kubernetes Data Analytics Data Management Virtual Agents Api Design Data Pipelines Docker

Requirements

Strong experience in Python application development.
Strong background in data engineering, data analytics, BI, or data application development.
Experience with cloud platforms and Kubernetes-based application/pipeline deployment.
Strong hands-on experience building production-grade data pipelines.
Strong understanding of data architecture, data processing, ETL/ELT, and data integration.
Experience working with databases, data warehouses, and/or modern data platforms.
Experience building data-facing applications or applications that interact directly with data and analytics platforms.
Hands-on experience developing LLM-powered applications.
Understanding of AI agent/harness engineering patterns and LLM application architecture.
Experience working with APIs, databases, data platforms, and enterprise data sources.
Cloud development experience with Kubernetes-based deployment.
Strong software engineering fundamentals including Git, testing, CI/CD, and production deployment.
Demonstrated ability to work independently and take ownership from requirements through delivery.
Strong analytical, troubleshooting, and communication skills.

Preferred Qualifications

Experience with agentic AI, LLM orchestration, RAG, or tool-using agents.
Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies.
Experience with vector databases and retrieval pipelines.
Experience integrating LLMs with enterprise data platforms.
Experience building dashboards, analytics applications, self-service data applications, or other data-centric user experiences.
Experience with AWS, Azure, or GCP.
Experience with Docker and Kubernetes.