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# Principal Data Architect

- **Company:** Acunor Inc
- **Location:** Hackensack, NJ, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Architecture, Data Governance, Data Infrastructure, Machine Learning, Cloud Services, Search Technologies, Enterprise Data Management, Google Cloud, Data Ingestion, Snowflake, Generative AI, Data Lineage, Data Management, Machine Learning Operations, Virtual Agents, Databricks
- **Published:** June 4, 2026
- **Apply:** https://www.careerjet.com/jobad/us39bd2ebbec9e1f26dc3cbfdb5c672102

## About the Role

Strong experience in cloud platforms: AWS, Azure, or Google Cloud
Deep expertise in modern data platforms: Snowflake, Databricks
Hands-on experience in AI/ML architecture, including GenAI and RAG
Knowledge of vector databases (e.g., Pinecone, FAISS, or equivalent)
Experience with MLOps/LLMOps tools and frameworks
Strong understanding of data governance, privacy, and compliance standards
Proven ability to design and scale enterprise data platforms
Leadership & Stakeholder Management
Provide architectural leadership across multiple programs and portfolios
Collaborate with business, engineering, and AI teams to align architecture with business outcomes
Mentor senior engineers and architects on best practices
Preferred Qualifications
Experience in BFSI or regulated industries
Exposure to large-scale AI transformation initiatives
Certifications in cloud or data platforms (AWS/Azure/GCP/Snowflake/Databricks)

## Description

1. Data Platform Architecture
Architect scalable, high-performance cloud data platforms across hyperscaler's (AWS, Azure, Google Cloud).
Design and implement modern data stack solutions leveraging technologies such as Snowflake and Databricks.
Define data ingestion, transformation, and serving architectures supporting real-time and batch workloads.
Drive standardization of data architecture patterns across the organization.

 2. AI & Machine Learning Architecture
Design and implement architectures for:
Generative AI solutions
Retrieval-Augmented Generation (RAG)
Vector databases and semantic search frameworks
Agentic AI frameworks and orchestration patterns
Define and operationalize MLOps and LLMOps pipelines for model lifecycle management.
Enable scalable deployment and monitoring of AI/ML models in production environments.

 3. Data Governance, Security & Compliance
Establish enterprise-wide data governance frameworks covering:
Data quality and validation standards
Data lineage and traceability
Master Data Management (MDM)
Implement AI governance controls to:
Mitigate model hallucinations
Ensure explainability and reliability
Protect data privacy and regulatory compliance
Define access control, encryption, and security best practices for data and AI platforms.


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