At a glanceSummarised by Seekless from the posting.
Must have7
8+ years of experience across cloud security, platform security, governance engineering, security architecture, data engineering, observability, analytics engineering, ML evaluation, or AI application monitoring
Strong understanding of identity, network controls, secrets management, logging, audit trails, data classification, least-privilege design, and evidence capture
Familiarity with retrieval-augmented generation, embeddings, vector stores, metadata, indexing, citation, access control, and knowledge-source quality
Ability to translate policy, risk, quality, and observability requirements into practical engineering controls and metrics
Strong software, data engineering, automation, or analytics engineering skills
Demonstrated ability to use AI tools as a practical system-building accelerator for governance engineering, analysis, dashboard development, evaluation, documentation, or control review
Strong documentation and communication skills for control standards, decision records, dashboards, exception patterns, and audit evidence
Nice to have6
Experience with AI governance, model risk management, LLM application security, agent security, or data protection for AI systems
Experience with Amazon Bedrock, AWS IAM, CloudTrail, CloudWatch, PrivateLink, KMS, VPC design, OpenSearch, vector databases, BI tools, or observability platforms
Experience with LLM evaluation, prompt evaluation, retrieval evaluation, golden datasets, regression testing, or AI quality frameworks
Experience with Temporal or comparable workflow orchestration platforms for approval flows, evidence capture, evaluation workflows, or operational reporting
Experience with enterprise knowledge systems, document repositories, metadata governance, search relevance, or permission-aware retrieval
Experience with PHI, PII, client-confidential, regulated, or sensitive-data environments
Eligibility1
Flexible living locations across the US. Ability to travel as needed
Skills
Amazon Bedrock
AWS IAM
CloudTrail
CloudWatch
PrivateLink
KMS
VPC
OpenSearch
vector databases
BI tools
observability platforms
Temporal
Benefits
Health insurance
Dental coverage
Vision coverage
About the company
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.
Join our team as the expert you are now and create your future.
About the role
The AI Security, Governance, Engineering and Observability role will translate security, privacy, compliance, architecture, and business requirements into executable platform controls while also establishing the first patterns for Huron Knowledge enablement, evaluation, telemetry, dashboards, audit evidence, and operational reporting.
Key Responsibilities
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Translate security, privacy, compliance, and architecture requirements into executable controls for AI workloads.
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Define workload classification patterns and required controls for each class.
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Establish prompt, response, embedding, retrieval, logging, retention, redaction, and client data segregation patterns in partnership with control functions.
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Define audit evidence patterns for model access, data movement, retrieval, tool calls, approvals, exceptions, and operational events.
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Design identity, secrets, network, sandbox, logging, and approval-gate patterns for AI applications and agents.
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Build governed knowledge patterns for authoritative sources, ingestion, indexing, metadata, access control, freshness, citation, and retrieval evaluation.
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Help select the first Huron Knowledge domain, source, or integration pattern for MVP validation.
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Define and implement retrieval quality metrics, model evaluation patterns, regression checks, operational telemetry, dashboards, and quality reporting.
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Partner with infrastructure engineers to implement controls, evidence, and reporting through automation rather than manual processes.
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Help teams understand whether AI systems are producing useful, grounded, safe, auditable, and cost-effective outputs.
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Use AI tools hands-on to accelerate control design, policy mapping, knowledge analysis, evaluation design, dashboard development, documentation, and evidence review.
Required Qualifications
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8+ years of experience across cloud security, platform security, governance engineering, security architecture, data engineering, observability, analytics engineering, ML evaluation, or AI application monitoring.
•
Strong understanding of identity, network controls, secrets management, logging, audit trails, data classification, least-privilege design, and evidence capture.
•
Familiarity with retrieval-augmented generation, embeddings, vector stores, metadata, indexing, citation, access control, and knowledge-source quality.
•
Ability to translate policy, risk, quality, and observability requirements into practical engineering controls and metrics.
•
Strong software, data engineering, automation, or analytics engineering skills.
•
Demonstrated ability to use AI tools as a practical system-building accelerator for governance engineering, analysis, dashboard development, evaluation, documentation, or control review.
•
Strong documentation and communication skills for control standards, decision records, dashboards, exception patterns, and audit evidence.
Preferred Qualifications
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Experience with AI governance, model risk management, LLM application security, agent security, or data protection for AI systems.
•
Experience with Amazon Bedrock, AWS IAM, CloudTrail, CloudWatch, PrivateLink, KMS, VPC design, OpenSearch, vector databases, BI tools, or observability platforms.
•
Experience with LLM evaluation, prompt evaluation, retrieval evaluation, golden datasets, regression testing, or AI quality frameworks.
•
Experience with Temporal or comparable workflow orchestration platforms for approval flows, evidence capture, evaluation workflows, or operational reporting.
•
Experience with enterprise knowledge systems, document repositories, metadata governance, search relevance, or permission-aware retrieval.
•
Experience with PHI, PII, client-confidential, regulated, or sensitive-data environments.
Flexible living locations across the US. Ability to travel as needed.
Legal
The estimated base salary for this job is $190,000 - $265,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $237,000 - $357,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.