Manager, AI Solutions
Atlanta, GA · In-person · Permanent
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What the market looks like
We've tracked 500+ management-level postings in AI governance across the US over the last six months, with strong concentration in California, New York, and Texas. Organizations are building out governance and compliance infrastructure faster than they can hire, and the strongest candidates bring hands-on experience operationalizing AI risk frameworks, not just theoretical knowledge. Compensation for this cohort ranges from $180K to $320K, with variation based on sector, scope, and whether the role sits within an existing AI organization or a nascent one. Professional services, technology, financial services, and healthcare are among the sectors most actively hiring for this profile.
Job responsibilities
Own the design and rollout of AI governance policies, risk assessment templates, and compliance workflows across the organization
Partner with AI/ML engineering, product, and legal teams to embed governance into development cycles and ensure models meet regulatory and internal standards before deployment
Build and lead a small governance team; define their workflows, success metrics, and training needs
Establish AI risk monitoring and audit processes; track model performance drift, bias, and compliance violations in production
Develop governance documentation and training for non-technical stakeholders (executives, board, business units)
Stay current on evolving AI regulation (AI Act, state privacy laws, sector-specific rules) and translate requirements into operational guardrails
Drive cross-functional working groups to identify emerging governance gaps and recommend remediation
Candidate requirements
5+ years building or operating governance, compliance, or risk management programs in technology, financial services, or regulated industries
Hands-on experience designing and implementing controls for algorithmic systems, data science models, or AI/ML products
Track record leading small teams and influencing across functions without direct authority
Working knowledge of machine learning concepts (model training, evaluation, inference) sufficient to assess risk and ask technical questions
Strong written and verbal communication skills; comfort presenting governance findings and recommendations to both technical and executive audiences
Familiarity with one or more AI governance frameworks (NIST AI RMF, ISO/IEC standards, or in-house equivalents) or experience building one from scratch