AI Security Architect

•  Strong background in cybersecurity, particularly Application Security, Product Security, Cloud Security, or Security Architecture.
• Understanding of Generative AI, LLM-based solutions, and their security and risk implications.
• Experience conducting security reviews, architecture assessments, and threat modelling.
• Experience defining and validating technical security controls.
• Knowledge of IAM, API security, secrets management, logging, monitoring, and auditability.
• Strong understanding of data protection, privacy, data minimisation, and the secure handling of sensitive or regulated information.
• Ability to translate AI security risks into clear, actionable technical requirements.
• Strong risk identification, prioritisation, and decision-making skills.
• Ability to challenge technical implementations and verify that security controls are effective.
• Strong collaboration skills and the ability to work with security, product, engineering, and data teams.
• A pragmatic approach focused on enforceable, measurable, and auditable security controls.

• Experience reviewing AI workloads in AWS and/or Azure environments.
•  Familiarity with AWS Bedrock, Azure OpenAI, Azure AI Foundry, or similar GenAI platforms.
• Knowledge of AI platform security controls, including guardrails, content filtering, IAM, least privilege, logging, observability, and data protection.
• Familiarity with Databricks security, including access controls, workspace permissions, jobs, pipelines, notebooks, secrets, networking, and data governance.
• Knowledge of the OWASP Top 10 for LLM Applications and security risks associated with Agentic AI.
• Familiarity with the Secure AI Framework (SAIF).
• Understanding of Model Context Protocol (MCP), agent-to-tool security, and Agent-to-Agent (A2A) architectures.
• Awareness of emerging AI security risk taxonomies, including MCP-38.
• Exposure to DevSecOps and/or MLOps environments.
• Experience establishing AI security standards or governance frameworks.

• A workplace that values innovation and personal growth.
• Opportunities to work on high-impact projects for leading clients.
• Flexible hours and hybrid work options.
• Support for professional development, including training and certifications.
• Health and life insurance.
• 25 days of annual leave.

Meet Genesis

Genesis Digital Solutions is a consulting and technology company that helps organizations turn ideas into measurable business impact through digital expertise and AI innovation. Founded in 2018, we operate across Portugal and the United Arab Emirates, supporting clients in multiple industries with intelligent, scalable, and secure solutions. We combine strategy, engineering, and emerging technologies such as AI, Data, Web3, and Digital Product Development to drive transformation end to end. Our focus is on building practical, human-centered solutions and long-term partnerships that enable sustainable growth, innovation, and real-world results.

Your Next Challenge

We are looking for an AI Security Architect to support the Product Security team, with a particular focus on Data and AI. You will help ensure that AI and Generative AI solutions are designed, integrated, and deployed securely, translating emerging AI security risks into practical technical requirements and enforceable controls.

Combining a strong cybersecurity background with an understanding of GenAI, LLM-based solutions, and Agentic.

What You’ll Work On

What We’re Looking For

Why You’ll Stay

• Review AI and GenAI use cases throughout the solution lifecycle, from initial design to production.
•  Define security requirements for AI platforms, APIs, agents, tool usage, and system integrations.
• Conduct security architecture reviews, threat modelling, and technical security assessments of AI solutions.
• Identify and mitigate risks such as prompt injection, jailbreaks, sensitive information disclosure, data leakage, unsafe tool use, excessive agent autonomy, and insecure integrations.
• Assess AI deployments against security, privacy, logging, monitoring, and auditability requirements.
•  Define security guardrails for the use of sensitive, personal, confidential, and regulated data.
• Review IAM, API security, secrets management, access controls, and least-privilege implementations.
• Validate logging, monitoring, audit trails, and evidence of control effectiveness.
• Contribute to AI security governance, technical standards, and secure-by-design patterns.
• Review AI integrations and agent architectures, including agent-to-tool interactions and trust boundaries.
• Collaborate with Product Security, cybersecurity, engineering, product, and data teams to ensure security controls are effectively implemented.
• Challenge technical designs and recommend practical measures to reduce AI-related security risks.