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Principal Technical Architect -

AI, Data Platforms & Cyber Security Position Title:

Principal Technical Architect -

AI Systems, Data Platforms & Cyber Security 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, Google Cloud Platform), 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 Category Required Skills & Technologies

AI & LLM

Systems Multi-agent frameworks, OpenAI APIs, Databricks Model Serving, Async Python (aiohttp), Pydantic, Prompt Engineering, Streamlit Cyber Risk & AI Security LLM Threat Modeling (Prompt Injection, Jailbreaking), Guardrails, OAuth 2.0 / Entra ID / Okta, Service Principals, Delta Sharing Security Data Governance & Security Unity Catalog (RLS, Dynamic Column Masking, PII/PCI classification), Zero-Trust Access Patterns, SIEM logging & audit trails Data Engineering & Platforms Databricks (Unity Catalog, Workflows, Delta Lake, Jobs API), PySpark, Data Observability, SQL / Relational Databases Cloud & FinOps AWS, Azure, Google Cloud Platform, Cloud Security Architecture, Cloud Cost Governance / FinOps frameworks Languages & Core Tech Python (Advanced Async), C#, .NET Core, SQL, REST API Architecture, YAML rule engines

Governance & Licensing 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.