SAP S/4HANA SD Consultant HTC Global Services Inc
Dearborn, MI30 days ago
Highlights
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. 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).
Numbers & Facts
| Location | Southfield, MI |
Description
Role: Senior Principal Technical Architect – AI, Data Platforms & Cyber Security
Location- Princeton, NJ & NYC, NY (Hybrid)
Fulltime
Job Description: 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, 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
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, GCP, 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.
Location- Princeton, NJ & NYC, NY (Hybrid)
Fulltime
Job Description: 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, 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
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, GCP, 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.