Senior AI Engineer – AI Governance

Location: Onsite (Eden Prairie, Minnesota, United States)

Employment Type: Contract

Experience: 8+ years

Work Arrangement: OnsiteJob Summary

Key Responsibilities: Design, develop, and deploy scalable AI/ML and Generative AI solutions for enterprise applications. Develop and integrate LLM-based applications, AI agents, RAG pipelines, embeddings, vector databases, and AI APIs. Work with models and platforms such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, or similar AI platforms. Develop AI solutions using Python and modern AI/ML frameworks and libraries. Design and implement production‐ready AI architectures across cloud environments. Build and maintain MLOps/LLMOps pipelines for model development, evaluation, deployment, monitoring, and lifecycle management. Optimize AI systems for performance, scalability, reliability, cost, and security. Implement automated testing and evaluation frameworks for AI/ML and GenAI applications. AI Governance & Responsible AIDevelop and implement an enterprise‐wide AI Governance Framework covering the complete AI lifecycle. Establish governance processes for AI use‐case identification, risk assessment, approval, development, deployment, monitoring, and retirement. Define and maintain AI policies, standards, procedures, and technical controls. Establish AI risk classification and assessment methodologies based on business impact, data sensitivity, model capabilities, and potential risks. Develop governance controls for Generative AI, LLMs, AI agents, and third‐party AI services. Establish processes for model documentation, including model cards, system documentation, data lineage, intended use, limitations, and risk assessments. Define requirements for human oversight, explainability, transparency, accountability, and responsible AI usage. Establish processes for evaluating AI models for bias, fairness, accuracy, robustness, safety, and reliability. Develop controls to identify and mitigate risks such as hallucination, prompt injection, data leakage, model misuse, unauthorized AI usage, and harmful outputs. Implement AI monitoring and governance mechanisms throughout the model/application lifecycle. Establish governance requirements for AI‐generated content, automated decision‐making, and AI‐assisted business processes.

AI Security & Privacy: Work with security teams to establish secure AI architecture and deployment practices. Define security controls for LLMs, AI agents, APIs, vector databases, prompts, embeddings, and AI infrastructure. Implement controls to protect sensitive and confidential information from unauthorized exposure through AI systems. Establish governance around AI data usage, data classification, retention, access control, and privacy. Evaluate third‐party AI providers, models, APIs, and AI services from security, privacy, and governance perspectives. Support AI threat modeling and security assessments. Address risks such as prompt injection, jailbreaks, data exfiltration, model poisoning, insecure plugins/tools, and unauthorized model access. Develop standardized AI/ML model evaluation and validation frameworks. Define model performance and risk thresholds before production deployment. Establish processes for model validation, testing, approval, monitoring, and periodic review. Define and track AI governance KPIs, KRIs, and risk indicators. Maintain appropriate audit trails and evidence for AI systems. Conduct periodic AI risk reviews and support internal/external audits.

Compliance & Regulatory Alignment: Monitor emerging AI laws, regulations, standards, and industry requirements and assess their impact on the organization's AI systems. Align AI governance practices with applicable frameworks and standards such as:

• NIST AI Risk Management Framework (AI RMF)ISO/IEC 42001 – AI Management SystemISO/IEC 23894 – AI Risk ManagementISO/IEC 27001SOC 2Applicable data privacy and AI regulations
• Translate regulatory and governance requirements into practical technical controls.
• Work with Legal, Compliance, Security, and Risk teams to maintain AI governance documentation.
• Support AI‐related audits, assessments, customer questionnaires, and regulatory inquiries.
• Establish an AI inventory/registry to track models, AI applications, use cases, owners, data sources, vendors, risks, and approval status.
• Define an AI approval process for new AI initiatives.
• Establish governance for shadow AI and unauthorized use of external AI tools.
• Develop enterprise guidelines for employee use of ChatGPT, Copilot, Gemini, Claude, and other GenAI tools.
• Establish controls for third‐party and open‐source AI models.
• Work with business teams to identify opportunities for responsible AI adoption.
• Provide technical guidance and training on AI governance and responsible AI practices.

Ideal Candidate Profile: The ideal candidate should be a hands‐on Senior AI Engineer who also understands governance and risk. This is not intended to be a purely compliance‐oriented position. The candidate should be capable of taking an AI use case from architecture and development through deployment, evaluation, monitoring, risk assessment, governance, and ongoing lifecycle management.