IT Compliance and AI Governance Consultant

LTD Global
United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience required
1 year minimum
Compensation
$124,800.0 - $131,040.0
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Big Data Software as a Service Cloud Computing Cyber Security Information Systems Computer Literacy Data Governance Data Infrastructure Data Stores Information Lifecycle Management
+16 more
Machine Learning Operational Data Store Role-Based Access Control Data Streaming Technical Data Management Systems Unstructured Data Privacy Controls Data Classification IT General Controls (ITGC) Large Language Models Information Technology Data Lineage Deployment Automation Integration Frameworks Data Management Api Management

Job description

We are seeking a highly skilled and strategic IT Compliance & AI Governance Consultant to partner directly with our client CTO office. In this role, you will be the foundational architect responsible for updating, scaling, and managing our corporate IT security frameworks and data governance models.

Your primary mandate will be to bridge departmental silos, conducting deep-dive discovery across the organization to map data flows and establish an accurate data inventory. You will ensure our technology ecosystem safely accommodates, governs, and scales Artificial Intelligence (AI) and Machine Learning (ML) initiatives, while maintaining bulletproof alignment with global security and privacy standards., 1. Cross-Functional Discovery & Data Inventory

  • Departmental Interviews: Conduct structured interviews and workshops with various department heads (e.g., Product, Engineering, Marketing, HR, Legal, and Sales) to comprehensively audit, discover, and document the flow of structured and unstructured data across the organization.
  • Data Flow Mapping: Build and maintain an enterprise-wide data inventory and data lineage map, specifically identifying where sensitive data is stored, how it is ingested, and how it migrates across different systems.
  • Shadow IT & AI Detection: Proactively identify and catalogue unauthorized ā€œshadowā€ AI tools, SaaS platforms, and data repositories currently utilized by various business units.
  1. AI & Data Governance Framework Architecture * Adapt Data Frameworks: Redesign and expand the existing enterprise data governance framework to address specific AI risks (e.g., data lineage, synthetic data usage, retrieval-augmented generation (RAG) pipelines, and model training inputs) discovered during the inventory process. * Ethical & Responsible AI: Establish policies surrounding algorithmic fairness, bias mitigation, explainability (XAI), transparency, and intellectual property (IP) protection regarding generative AI. * Data Lifecycle Management: Define clear rules for data classification, minimization, retention, and isolation, specifically ensuring proprietary data is not leaked into public LLM training sets.

  2. Security & Compliance Integration * Framework Alignment: Manage, maintain, and map IT controls across core security frameworks such as SOC 2 Type II, ISO/IEC 27001, and NIST CSF. * Incorporate AI Security Standards: Integrate emerging AI security frameworks, specifically ISO/IEC 42001 (Artificial Intelligence Management System) and the NIST AI Risk Management Framework (AI RMF), into the broader corporate compliance program. * Regulatory Mapping: Ensure continuous adherence to evolving global regulations, including GDPR, CCPA/CPRA, and emerging AI-specific laws (e.g., the EU AI Act).

  3. Risk Assessment & Third-Party Oversight * AI Risk Assessments: Conduct comprehensive impact assessments on all internal and product-facing AI deployments to identify security vulnerabilities, potential model drift, and compliance gaps. * Vendor Vetting: Evaluate third-party AI vendors, APIs, and SaaS tools. Formulate a vetting protocol to approve or deny incoming AI tech stacks based on security and data privacy mandates.

Requirements

Do you have experience in Stakeholder relationship building?, Do you have a Master’s degree?, * Experience: 3-6+ years of experience in IT compliance, information security auditing, data governance, or technology risk management.

  • AI/ML Familiarity: Minimum 1-2 years of hands-on experience dealing with data privacy/governance issues explicitly related to cloud-native environments, big data pipeline architecture, or AI/ML model deployment.
  • Education: Bachelor’s degree in information technology, Cybersecurity, Legal/Compliance, Data Science, or a related field (Master’s or JD a plus).

Technical & Leadership Skills

  • Stakeholder Management & Discovery: Proven ability to interview diverse department leads, translate complex technical data workflows from engineering, and extract operational data usage habits from non-technical business units.
  • Framework Expertise: Deep expertise in mapping controls for SOC 2, ISO 27001, and NIST. Strong familiarity with ISO/IEC 42001 and the NIST AI RMF.
  • Technical Literacy: Ability to understand data infrastructure, API integrations, LLM guardrails, and access control models (RBAC/ABAC).

Preferred Certifications (One or more)

  • IAPP Certified AI Governance Professional (AIGP) (Highly preferred).
  • CISA (Certified Information Systems Auditor) or CRISC (Certified in Risk and Information Systems Control).
  • CIPP/US/E (Certified Information Privacy Professional).
  • CDMP (Certified Data Management Professional).

Benefits & conditions

$60 - $63 an hour - Contract

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:40 min

Addressing data sovereignty and compliance blind spots within AI

Sebastian Kister Sebastian Kister Ā· WWC Europe 2026

1:53 min

Mapping end-to-end data lineage for regulatory compliance

Ferd Scheepers Ā· WWC 2022

2:20 min

Decoupling user data with decentralized web nodes

Angie Jones Angie Jones Ā· WWC 2023

3:28 min

Defining big data and machine learning fundamentals

Ayon Roy Ā· LIVE

1:32 min

Understanding fast, accurate, and big data store trade-offs

Philipp Krenn Ā· WWC 2022

1:47 min

Navigating regulatory compliance and data sovereignty in enterprise AI

Florian Deter Florian Deter +4 Ā· WWC Europe 2026

Videos

See all

Related articles

See all