A
Senior AI Engineer – AI Governance
Abotts Inc
3 hours ago
Full-time
On-site
Eden Prairie, Minnesota, United States
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 .
#J-18808-Ljbffr
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 .
#J-18808-Ljbffr