Description

Role Overview
We are seeking an experienced AI Architect to define and implement enterprise standards for how AI models, GenAI applications, agents, prompts, retrieval systems, data, tools, and third-party AI products are selected, evaluated, approved, secured, monitored, and retired.

The role sits at the intersection of AI architecture, governance, cybersecurity, privacy, risk, product evaluation, and enterprise architecture. The architect will establish practical guardrails that allow product and engineering teams to innovate while managing security, privacy, regulatory, content-rights, operational, vendor, and cost risks.  AI Governance, AI Security, Evaluation/Red Teaming, and Product/Vendor Evaluation

Target senior AI Architects with enterprise GenAI governance/security experience—not just GenAI developers.

Strong preference for candidates with production AI governance + security + AI product/vendor evaluation experience.

Candidates should have hands-on exposure to LLMs, RAG, AI agents, prompt security, AI evaluation/red teaming, cloud AI platforms, and enterprise controls.

Must be comfortable working across Cybersecurity, Privacy, Legal, Enterprise Architecture, Product, and Procurement.

Candidates with experience in large enterprise, media/telecom, streaming, content, customer-facing AI, or similarly regulated/high-volume environments can be particularly relevant.

Key Must-Haves

Candidates should demonstrate hands-on experience in most of the following:

• 10+ years in enterprise architecture, security architecture, technology risk, data/platform architecture, or related areas.
• Strong production AI/ML or Generative AI governance experience.
• Experience designing governance/control frameworks for:
  • LLMs/foundation models
  • Prompts
  • RAG/retrieval and vector databases
  • AI agents and tool calling
  • AI APIs and cloud AI services
  • AI data and third-party AI vendors
• Strong understanding of AI security and threat modeling, including:
  • Prompt injection/jailbreaking
  • Data exfiltration and privacy leakage
  • Excessive/unsafe agent autonomy
  • Tool abuse
  • Identity & authorization
  • RAG poisoning
  • AI/model supply-chain risks
• Experience with AI evaluation and red teaming, including accuracy, groundedness, relevance, safety, bias, toxicity, refusal behavior, security, latency, availability, and cost.
• Experience establishing AI governance controls, approval workflows, risk tiers, auditability, monitoring, rollback, and emergency disablement.
• Strong knowledge of cloud security, IAM, encryption, secrets management, data protection, logging, observability, and incident response.
• Experience evaluating AI products/vendors through POCs/POVs, technical due diligence, RFPs, product comparisons, and build-vs-buy analysis.
• Ability to translate governance and risk requirements into practical architecture patterns, technical controls, acceptance criteria, and implementation standards.
• Experience working with Cybersecurity, Privacy, Legal, Data Governance, Procurement, Enterprise Architecture, and Product teams

Key Skills

Education

Any Graduate