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Open nowPosted 3 days ago

Technical Consultant - Data and AI Security

IBM2,109 open roles

Where
No City, US
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Your applicationOpen nowTechnical Consultant - Data and AI SecurityIBM · No City, US

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 12 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 3 days ago

IBM median: 12 days open

The posting

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact.

We are seeking a visionary AI Security Architect to lead the design, assessment, and governance of security frameworks for AI/ML systems across the enterprise. You will be the subject matter expert at the intersection of artificial intelligence, cybersecurity, and regulatory compliance — ensuring AI systems are built, deployed, and operated securely and responsibly.

You will work across Client's cross-functionally with data science, engineering, legal, compliance, and executive leadership to define how the organization builds trustworthy AI — from threat modeling to policy enforcement to third-party AI vendor risk.

Key Responsibilities

AI Security Architecture

  • Design and advise Clients on enterprise-wide security reference architectures for AI/ML systems — LLMs, generative AI pipelines, ML inference infrastructure.
  • Establish secure-by-design patterns for model training, data pipelines, model registries, APIs, and AI-enabled applications.
  • Define security controls for AI infrastructure across cloud, on-prem, and hybrid environments (Azure, AWS, GCP).

AI Governance & Policy

  • Develop and enforce the organization's AI Governance Framework, including acceptable use policies, model risk management, data ethics, and accountability structures.
  • Align AI governance programs with regulations and standards: EU AI Act, NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS.
  • Define AI risk tiers and corresponding control requirements (high-risk vs. limited-risk AI systems).
  • Establish model cards, AI Bill of Materials (AI-BOM), and audit trail requirements for deployed AI systems.
  • Partner with Legal, Privacy, and Compliance on regulatory obligations for AI across domains.

AI Security Assessments

  • Lead AI-specific threat modeling using MITRE ATLAS, OWASP LLM Top 10, and adversarial ML taxonomies.
  • Conduct assessments covering: data poisoning, model inversion, adversarial evasion, prompt injection, training data extraction, supply chain risks, and model theft.
  • Assess AI/ML vendor products and third-party AI integrations for security posture and governance maturity.
  • Co-ordinate and run red team exercises — LLM jailbreaking, API abuse, agentic AI manipulation scenarios.
  • Produce written assessment reports with risk ratings, findings, and remediation roadmaps for technical and executive audiences.

This role can be performed from anywhere in the US.

  • 7+ years in cybersecurity, with 3+ years focused on AI/ML security, data science security, or emerging technology risk.
  • Deep understanding of ML concepts: model training, inference, fine-tuning, RAG architectures, agentic AI, and LLM deployment.
  • Hands-on experience with AI/ML threat modeling and adversarial attack techniques (data poisoning, prompt injection, adversarial examples, model extraction).
  • Familiarity with AI governance frameworks: NIST AI RMF, ISO/IEC 42001, EU AI Act, OWASP LLM Top 10, MITRE ATLAS.
  • Strong background in cloud security architecture (AWS, Azure, or GCP) — IAM, data security, network controls.
  • Experience conducting security architecture reviews, risk assessments, and writing executive-level reports.
  • Excellent written and verbal communication — ability to translate complex AI risks into business-relevant language.
  • Experience with MLOps platforms (MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML).
  • Programming skills in Python for scripting, security automation, or ML experimentation.
  • Prior experience building or auditing Responsible AI / Trustworthy AI programs.
  • Experience with AI supply chain security — model provenance, SBOM/AI-BOM, open-source model vetting.
  • Background in regulated industries (finance, healthcare, government).
  • Relevant certifications: CISSP, CCSP, CISM, AWS/Azure/GCP Security Specialty, or GIAC certifications.
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