IT Governance Engineer - Virtual

Our story

At Alight, we believe a company’s success starts with its people. At our core, we Champion People, help our colleagues Grow with Purpose and true to our name we encourage colleagues to “Be Alight.”

Our Values:

• Champion People – be empathetic and help create a place where everyone belongs.
• Grow with Purpose – Be inspired by our higher calling of improving lives.
• Be Alight – act with integrity, be real and empower others.

It’s why we’re so driven to connect passion with purpose. Alight helps clients gain a benefits advantage while building a healthy and financially secure workforce by unifying the benefits ecosystem across health, wealth, wellbeing, absence management and navigation.

With a comprehensive total rewards package, continuing education and training, and tremendous potential with a growing global organization, Alight is the perfect place to put your passion to work.

Join our team if you Champion People, want to Grow with Purpose through acting with integrity and if you embody the meaning of Be Alight.

Learn more at

We are seeking an experienced AI Governance Engineer to design, build, and automate the technical controls and processes that underpin our enterprise AI Governance program. This role will also serve as a key security architect for AI systems, ensuring robust cybersecurity, risk management, and compliance across all AI initiatives. You will collaborate closely with cybersecurity, privacy, and legal teams to safeguard AI assets and data, and to ensure responsible, safe, and compliant use of AI throughout the organization.

• Technical Governance Architecture: Design and implement automated inventory collection, monitoring, and risk scoring for all AI systems, models, and tools.
• Security Integration: Architect and enforce security controls for AI systems, including access management, data protection, vulnerability scanning, and incident response protocols.
• Workflow Integration: Build and integrate governance controls, approval of workflows, and checkpoints into SDLC, and engineering pipelines.
• Automation & Tooling: Develop for auditing, scanning, and evaluating AI models (e.g., for toxicity, drift, hallucinations). Implement model documentation standards (model cards, evaluation pipelines).
• Cyber
  
  Risk Management:
  
  Conduct risk assessments for AI systems, identify potential threats, and implement mitigation strategies. Collaborate with cybersecurity teams to ensure AI systems comply with organizational and regulatory security standards.
• Compliance & Oversight: Support evidence collection for audits (SOC2, ISO, internal/external regulators). Ensure technical controls meet regulatory and policy requirements.
• Collaboration: Work closely with Security, Privacy, Legal, Data Governance, and Engineering teams to embed governance and security into product and platform development.
• Continuous Improvement: Monitor emerging risks, regulatory changes, and industry best practices. Enhance automation, controls, and technical documentation as needed.
• Enablement: Support training and enablement initiatives by building toolkits and technical resources for engineers and business users.

• 5+ years in Dev Ops, MLOps, platform engineering, backend development, or cybersecurity, with a focus on automation and controls.
• Strong programming skills (Python preferred), experience with APIs, and automation frameworks.
• Experience integrating governance, risk, or compliance controls into engineering workflows.
• Deep understanding of cybersecurity principles, threat modeling, vulnerability management, and incident response.
• Familiarity with regulatory frameworks (NIST AI RMF, ISO 42001, SOC2, EU AI Act) and AI safety/fairness concepts.
• Experience building tools for model evaluation, monitoring, and documentation.
• Analytical, troubleshooting, and documentation skills.
• Excellent collaboration and communication skills.

• Experience with cloud security, identity and access management, and secure software development practices.
• Understanding of LLM architectures and AI model evaluation (bias, fairness, interpretability, drift, hallucinations).
• Experience with audit, evidence collection,…