Principal Data Architect

Acunor Inc
Hackensack, NJ, United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

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
+7 more
Snowflake Generative AI Data Lineage Data Management Machine Learning Operations Virtual Agents Databricks

Job 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.

Requirements

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)

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