Senior AI Engineer - AI Governance

Abotts Partners with singapore based tech giant to help migrate their public sector customer from Sybase to SQL server.

Abotts partners with NYPL to integrate with their partner libraries.

ABOTTS partners with County in Los Angeles to upgrade their court infrastructure into new technologies.

Upworks Inc partners with ABOTTS to build their Oracle Cloud Infrastructure (OCI) and migrate their custom applications to OCI.

Abotts partners with startup to manage and maintain their IT infrastructure and support SOC2 reporting.

Abotts Inc Partners with Gnorth consulting to deploy exadata and ODA for a large public sector customer.

Abotts Partners with singapore based tech giant to help migrate their public sector customer from Sybase to SQL server.

Abotts partners with NYPL to integrate with their partner libraries.

ABOTTS partners with County in Los Angeles to upgrade their court infrastructure into new technologies.

Upworks Inc partners with ABOTTS to build their Oracle Cloud Infrastructure (OCI) and migrate their custom applications to OCI.

Abotts partners with startup to manage and maintain their IT infrastructure and support SOC2 reporting.

Abotts Inc Partners with Gnorth consulting to deploy exadata and ODA for a large public sector customer.

Location: Onsite (Eden Prairie, Minnesota, United States)
Employment Type: Contract
Experience: 8+ years
Work Arrangement: Onsite

Job Summary

We are seeking a Senior AI Engineer – AI Governance to lead the design, implementation, and governance of enterprise AI and machine learning systems. The ideal candidate will combine strong hands‑on experience in AI/ML engineering, Generative AI, LLMs, cloud AI platforms, and MLOps with a strong understanding of AI governance, responsible AI, model risk management, security, privacy, and regulatory requirements .

The candidate will work closely with engineering, security, legal, compliance, data, and business teams to ensure that AI solutions are secure, explainable, transparent, reliable, compliant, and aligned with organizational policies and applicable regulations .

The role will involve establishing AI governance frameworks, developing AI risk and control mechanisms, evaluating AI models and applications, and supporting the responsible deployment of AI across the organization.

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 AI

• Develop 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 System
  • ISO/IEC 23894 – AI Risk Management
  • ISO/IEC 27001
  • SOC 2
  • Applicable 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 .

Required Skills & Experience

• 8+ years of experience in AI/ML engineering, data science, machine learning, or related technical roles .
• Strong hands‑on experience developing and deploying AI/ML and Generative AI applications .
• Strong experience with Python and modern AI/ML frameworks.
• Strong understanding of LLMs, RAG, embeddings, vector databases, AI agents, prompt engineering, and model evaluation .
• Experience with one or more major cloud AI platforms such as Azure, AWS, or Google Cloud .
• Experience with MLOps/LLMOps, CI/CD, model deployment, monitoring, and lifecycle management .
• Strong understanding of AI security, privacy, responsible AI, and model risk management .
• Experience designing or implementing AI governance frameworks, policies, standards, and controls .
• Strong understanding of AI risks including:
  • Bias and fairness
  • Hallucination
  • Explainability
  • Data privacy
  • Prompt injection
  • Model security
  • Data leakage
  • Model drift
  • Third‑party AI risk
  • AI misuse
• Experience working with cross‑functional teams including Engineering, Security, Legal, Compliance, Risk, and Business teams .

Preferred Qualifications

• Experience implementing NIST AI RMF or ISO/IEC 42001 .
• Experience with SOC 2, ISO 27001, or enterprise security governance .
• Experience with AI risk management or model governance platforms.
• Experience building AI governance dashboards, inventories, and assessment workflows.
• Experience with Azure AI Foundry, Microsoft Copilot Studio, Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar platforms .
• Experience with AI red teaming, adversarial testing, or AI security assessments.
• Experience with enterprise Data Loss Prevention (DLP), IAM, SIEM, and security monitoring .
• Relevant certifications in AI, cloud, cybersecurity, risk, or governance are a plus.
• Generative AI & LLMs
• AI Governance
• Responsible AI
• AI Security
• Privacy & Data Governance
• MLOps / LLMOps
• Regulatory & Compliance Awareness
• Stakeholder Management

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 .