Cloud , Data and AI Solution Architect, Assistant Vice President

Who we are looking for

We are seeking an Assistant Vice President (AVP), Cloud / Data / AI Solution Architect to design scalable, secure, and business-aligned solutions across AWS cloud, data, cybersecurity, and advanced AI domains. This role will shape architectures that leverage GenAI, large language models, retrieval-augmented generation (RAG), agentic workflows, and AI orchestration patterns. The ideal candidate combines hands-on technical depth with strong architecture judgment and the ability to collaborate across engineering, cybersecurity, data, risk, and business stakeholders.

Why This Role is important to us

Cybersecurity teams increasingly rely on high-quality data, analytical models, and AI-enabled insights to prioritize risk, detect emerging issues, and respond effectively. This Data Scientist role strengthens the organization's ability to transform cybersecurity telemetry and operational data into predictive, explainable, and actionable intelligence. The role will help improve decision-making across security operations, risk management, vulnerability prioritization, threat detection, and enterprise cybersecurity reporting.

What you will be responsible forKey Responsibilities

• Design end-to-end solution architectures that integrate AWS cloud services, enterprise data platforms, application services, AI capabilities, cybersecurity controls, and operational workflows.
• Translate business, technology, cyber, and data requirements into practical architecture patterns, reference designs, technical roadmaps, and implementation guidance.
• Develop solution approaches for GenAI and LLM-enabled products, including RAG patterns, prompt and context strategies, knowledge retrieval, agentic workflows, AI orchestration, and human-in-the-loop operating models.
• Partner with data engineering and analytics teams to design scalable data ingestion, transformation, governance, metadata, lineage, and consumption patterns that support AI and enterprise analytics use cases.
• Collaborate with cybersecurity, risk, and control teams to embed secure-by-design principles, identity and access controls, logging, monitoring, data protection, and responsible AI considerations into solution designs.
• Create architecture artifacts such as conceptual diagrams, logical designs, integration patterns, data flows, sequence views, decision records, and executive-ready solution summaries.
• Evaluate technology options, identify trade-offs, and recommend architecture choices that balance scalability, cost, resiliency, security, maintainability, adoption, and time-to-value.
• Support delivery teams through design reviews, technical problem solving, proof-of-concept development, implementation planning, and transition from architecture into engineering execution.
• Contribute to enterprise standards, reusable patterns, architecture guardrails, and governance practices for cloud, data, and AI-enabled solutions.

Education & Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Cybersecurity, or a related discipline; equivalent professional experience may be considered.
• Mid-level professional experience designing or delivering cloud, data, application, analytics, cybersecurity, or AI-enabled technology solutions in an enterprise environment.
• Strong working knowledge of AWS architecture concepts, including cloud-native services, networking, security, identity, data services, application integration, resiliency, and operational best practices.
• Practical understanding of modern data architecture and engineering concepts, including data pipelines, data modeling, batch and streaming patterns, metadata, data quality, lineage, and governed data consumption.
• Knowledge of GenAI and AI solution patterns, including LLM application design, RAG, vector search concepts, agentic workflows, orchestration patterns, evaluation considerations, and responsible AI principles.
• Ability to incorporate cybersecurity and risk requirements into technical designs, including secure access, encryption, auditability, logging, monitoring, vulnerability considerations, and control alignment.
• Strong communication skills with the ability to explain architecture options, risks, assumptions, and recommendations to engineering teams, product owners, senior stakeholders, and governance forums.
• Experience creating clear architecture documentation, diagrams, technical narratives, implementation guidance, and decision records.

Preferred Skills and Experience

• Experience architecting AI-assisted workflows, enterprise copilots, intelligent automation, cyber analytics solutions, or data-driven decision platforms.
• Hands-on exposure to LLM platforms, model APIs, embedding models, vector databases, semantic search, knowledge grounding, workflow orchestration, or agent frameworks.
• Experience with enterprise integration patterns such as APIs, event-driven architecture, messaging, workflow platforms, identity federation, and service-oriented design.
• Familiarity with cybersecurity operations, vulnerability management, security analytics, SOC workflows, cloud security posture, or cyber risk reporting use cases.
• Experience with architecture governance, design review boards, solution assurance, reference architectures, technical standards, and reusable architecture patterns.
• Exposure to modern analytics, BI, or visualization platforms and the ability to design solutions that support both technical users and executive decision-makers.
• Relevant AWS, architecture, cybersecurity, data engineering, or AI certifications are beneficial but not required.

Technical SkillCloud ArchitectureAI / GenAIData PlatformsCybersecurityIntegration & Governance

AWS services, resiliency, networking, security, cost-aware design

LLMs, RAG, agentic workflows, AI orchestration, responsible AI

Pipelines, modeling, lineage, metadata, governed consumption

Secure-by-design, identity, logging, monitoring, controls

APIs, events, workflows, architecture standards, decision records

Cloud-native reference patterns and solution trade-offs

Prompt/context design, retrieval, evaluation, human-in-the-loop patterns

Batch/streaming concepts, quality checks, reusable data products

Cyber analytics, vulnerability and risk-aware design

Cross-functional collaboration, governance forums, delivery enablement

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