REQ: Senior Principal Technical Architect - AI, Data Platforms & Cyber Security in Princeton, NJ or New York City, NY (Hybrid Position)

Hi,

One of our clients is looking for Senior Principal Technical Architect - AI, Data Platforms & Cyber Security in Princeton, NJ or New York City, NY

Title: Senior Principal Technical Architect - AI, Data Platforms & Cyber Security

Location: Princeton, NJ or New York City, NY (Hybrid Position)

Duration: Full Time

Department: Enterprise Architecture / Data, AI & Security Engineering







Experience Level: 15+ Years (Executive / Principal Level)

Role Overview

We are seeking a visionary and hands-on Principal Technical Architect to lead the architecture, design, security, and strategic evolution of our Enterprise Data Platforms and Multi-Agent GenAI Systems. In this role, you will bridge the gap between complex enterprise data engineering, modern cloud architecture, cutting-edge Generative AI applications, and enterprise cybersecurity controls.

You will design zero-touch automated data observability solutions, multi-agent AI pipelines, zero-trust data access patterns, and enterprise-wide GenAI adoption frameworks. The ideal candidate brings a deep technical background in Databricks, cloud platforms, Python, contract-driven LLM architectures, threat modeling for AI systems, and proven enterprise leadership in scaling and securing AI solutions across the organization.

Key Responsibilities

1. AI Systems & Multi-Agent Architecture

• Architect Multi-Agent AI Pipelines: Design end-to-end, LLM-powered multi-agent frameworks (deterministic + generative) using Pydantic contracts, asynchronous Python, and provider-agnostic model integration (e.g., OpenAI SDK, Databricks Model Serving).
• AI Tooling & Scaffolding: Build graph-based execution builders, dynamic YAML rules engines, capability registry patterns, and structured diagnostic retry mechanisms for LLM agents.
• Human-in-the-Loop Integration: Implement validation and curation layers that enable user DAG editing, schema validation, and error repair before scaffold generation.

2. AI Security, Risk & Guardrails

• LLM Threat Modeling & Abuse Prevention: Lead abuse-case modeling, prompt injection defense, jailbreak mitigation, and red-teaming strategies for LLM agents and RAG architectures.
• Output Guardrails & Data Protection: Implement payload masking, custom SQL validation layers, row-count caps, and automated PII/PCI detection to prevent data exfiltration via AI interfaces.
• Identity & Access Governance: Architect hybrid identity flows (OAuth 2.0, Okta/Entra ID), Service Principal access patterns, and automated token lifecycle/rotation management (e.g., Delta Sharing tokens).

3. Enterprise Data Platforms & Observability

• Databricks Estate Architecture: Lead large-scale data platform migrations, estate auto-discovery, and governance automation across Databricks workspaces (Unity Catalog, Workflows, Jobs API, Delta Lake, Delta Sharing).
• Data Governance & Zero-Trust Access: Enforce fine-grained authorization models including Row-Level Security (RLS), dynamic column masking, and centralized data classification in Unity Catalog.
• Data Quality & Observability Frameworks: Design automated, multi-tiered data quality verification platforms capable of real-time incident detection, automated table onboarding, and continuous file freshness tracking.
• Platform Governance & FinOps: Oversee multi-cloud cost governance (AWS, Azure, GCP), resource optimization, and infrastructure governance to maximize ROI while maintaining compliance.

4. Enterprise GenAI Adoption & Governance

• Org-Wide Transformation: Define and execute adoption strategies for developer AI tooling (e.g., GitHub Copilot, custom LLM assistants) and establish measurement frameworks for code quality, productivity gains, and ROI.
• Enablement & Standards: Conduct technical workshops, build best-practices documentation, create reusable architectural patterns, and mentor engineering teams across divisions.
• Compliance & Security Auditing: Oversee access recertification, SIEM logging/auditing mechanisms, and regulatory compliance (e.g., regional data residency and vendor risk governance).

Required Qualifications & Technical Expertise

Professional Experience

• 10+ years of progressive experience in software engineering, enterprise data platforms, AI systems, and technical/security architecture within high-volume enterprise environments.
• Proven track record of architecting scalable solutions adopted across large organizations while maintaining high standards of data protection and zero-trust security.
• Experience leading enterprise-wide technology adoption programs, platform migrations, and security governance frameworks.

Technical Stack & Competencies

Required Skills & Technologies

Multi-agent frameworks, OpenAI APIs, Databricks Model Serving, Async Python (aio Pydantic, Prompt Engineering, Streamlit

LLM Threat Modeling (Prompt Injection, Jailbreaking), Guardrails, OAuth 2.0 / Entra ID / Okta, Service Principals, Delta Sharing Security

Unity Catalog (RLS, Dynamic Column Masking, PII/PCI classification), Zero-Trust Access Patterns, SIEM logging & audit trails

Databricks (Unity Catalog, Workflows, Delta Lake, Jobs API), PySpark, Data Observability, SQL / Relational Databases

AWS, Azure, GCP, Cloud Security Architecture, Cloud Cost Governance / FinOps frameworks

Python (Advanced Async), C#, .NET Core, SQL, REST API Architecture, YAML rule engines

Infrastructure & Licensing Governance, Enterprise Developer Tooling Administration, Token Lifecycle Management

Key Leadership Capabilities

• Strategic Vision & Execution: Ability to map enterprise business requirements into robust, secure, contract-first architectural patterns.
• Cross-Functional Influence: Proven record of evangelizing new technologies, driving culture changes, and presenting technical strategy and cyber risk postures to executive stakeholders.

Cost & Risk Optimization: Track record of driving cost efficiency and operational risk reduction while maintaining a rigorous security posture.