AI Security Architect

Equação IT is a company focused on boosting technology and solid solutions through our expert consultants leading a high perspective and adding value to our customers with the purpose of providing optimization and business growth to our partners.

We are looking for a AI Security Architect with the following requirements:

Key Responsibilities:

Support and review AI/GenAI use cases from design through production
Define security requirements for AI platforms, APIs, agents, tool use, and integrations
Perform architecture reviews, threat modelling, and security reviews of AI solutions
Validate integrations and deployments against security, privacy, logging, and auditability requirements

Identify and mitigate risks such as:

Prompt injection / jailbreaks
Sensitive information disclosure / data leakage
Unsafe tool use / excessive agency
Abusive or unbounded consumption
Insecure integrations or poisoned inputs
Define guardrails for data usage, especially for sensitive, personal, confidential, or regulated data
Ensure secure logging, monitoring, audit trails, and evidence of control effectiveness
Contribute to AI security governance, standards, and secure-by-design patterns

Required Experience:

Background in cybersecurity, ideally application security, product security, cloud security, or security architecture
Understanding of GenAI / LLM-based solutions from a security and risk perspective
Experience with security reviews, architecture reviews, threat modelling, and defining technical controls
Experience with IAM, API security, secrets management, logging, monitoring, and control validation
Knowledge of data protection, privacy, data minimization, and secure handling of sensitive or regulated data
Ability to challenge technical implementations and validate that controls are effectively applied

Preferred Experience:

Familiarity with AWS and/or Azure environments in the context of AI workload security
Exposure to AWS Bedrock, Azure OpenAI / Azure AI Foundry, or similar GenAI platforms, especially around:
Guardrails and content controls
IAM and least privilege
Logging, observability, and auditability
Data protection and sensitive data handling
Familiarity with Databricks as a platform to review from a security perspective, including:
Access control and data permissions
Workspaces, jobs, pipelines, and notebooks
Secrets, networking, and data governance

Familiarity with:

OWASP Top 10 for LLM Applications / Agentic AI
SAIF (Secure AI Framework)
Model Context Protocol (MCP) and agent-to-tool security considerations
Agent-to-Agent (A2A) architectures and agentic trust boundaries
Emerging risk taxonomies such as MCP-38
Exposure to DevSecOps and/or MLOps as a collaboration context

Core Skills:

Can translate AI security risks into clear technical requirements
Strong risk identification, prioritisation, and decision-making
Able to challenge and validate implementations effectively
Works well with security, product, engineering, and data teams
Focused on practical, enforceable, and auditable controls

Important Note:

This is a cybersecurity role applied to AI/GenAI. It is not primarily a model development, data engineering, or operational ownership role for pipelines, notebooks, or ML platforms. Support Position.

Location: Lisbon - Hybrid