AI Data Engineer
Siri InfoSolutions Inc
New York, NY, United States
about 2 months 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
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.
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Prepare application
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