AI IAM Engineer
Spectraforce
Mount Laurel, New Jersey
an hour ago
Job Description
Job Title: AI IAM Engineer
Location: Mount Laurel, NJ (Hybrid Position 4 days a week)
Duration: 6 Months
Job Description:
We are seeking an AI IAM & Infrastructure Sr. Engineer to be a key technical leader, bridging the gap between cutting-edge AI innovation and our core IAM framework. Your mission will be to drive forward-looking security strategies and engineering solutions for Generative AI and LLM platforms, while specializing in leveraging AI security capabilities to augment and fortify existing enterprise solutions
Key Responsibilities: Research, Evaluation, and Design
This role is primarily focused on providing AI Security/IAM Infrastructure solutions, researching, assisting in designing, and implementing solutions that mitigate gaps in security/IAM controls, and support leadership strategy and road maps. You will be responsible for conducting proof-of-concepts (PoC's) for new security technologies and protocols, and support hardening efforts to protect our mission-critical assets deployed across Azure, Google Cloud, and On-Premises environments.
1. Advanced Protocol and Application Security
Generative AI Protocols: Evaluate and secure emerging standards for multi-agent workflows, such as the Agent-to-Agent (A2A) and Model Context Protocol (MCP).
Threat Modeling: Support threat modeling exercises for new AI applications and pipelines to proactively identify design flaws and adversarial attack vectors (e.g., prompt injection paths).
Mitigation Solutions: Support the design, build, and testing of security controls to mitigate common AI/ML attacks as outlined by frameworks like the OWASP Top 10 for LLM Applications, Mitre Atlas.
2. Access, Identity, and Cloud Controls
IAM Design/implementation: Define and implement security designs for Identity and Access Management (IAM), specializing in securing non-human identities, service principles, and cross-cloud access.
API Security: Own the security strategy for all AI service consumption, including hardening of API Gateways and securing authentication flows (e.g., OAuth 2.0/OIDC) for model endpoints.
Secrets Management: Design and PoC the secure storage, injection, and rotation of confidential data (API keys, model weights, database credentials) using solutions like Azure Key Vault and GCP Secret Manager in support of AI Security Infrastructure initiatives.
AI Cloud Hardening: Establish security configuration baselines, AI IAM framework, and network segmentation (e.g., Private Link, VPC Service Controls) for AI-specific cloud resources on Azure and GCP.
3. Collaboration and Strategy Translation
AI Red Team Support: Provide essential infrastructure security expertise and tooling to support the AI Red Team program, helping them build secure testing environments and validate attack findings.
Translation to Production: Collaborate with IAM, DevOps, Governance, Vulnerability Management, and Platform Engineering partners to translate successful security PoC's and designs into robust, production-ready solutions and Infrastructure as Code (IaC) controls.
MUST-HAVE Hard Skills:
Technical Skills
1.Cloud KMS & Crypto
• KMS concepts (GCP KMS, Azure Key Vault, HSM vs software keys)
• Enveloping encryption patterns
• full Key lifecycle experience: create → rotate → revoke → audit
• IAM bindings at the key / key-ring level (not just project/subscription) & BYOK / CMEK integrations.
2. Non-Human Identity & Workload Identity -huge for agentic AI.
• Service accounts / managed identities
• Workload Identity Federation (OIDC)
• Token exchange flows (STS)
• Least-privilege scoping for automation and agents
3. Policy-as-Code & Guardrails for Low-code development + AI IAM guardrails
• Terraform modules (inputs → opinionated defaults)
• policy engines (OPA, Sentinel, Azure Policy)
• Preventing key misuse via design/AI IAM framework module
4. Low-Code / Integration Fluency/AI
• Azure Data Factory
• Logic Apps / Power Automate
• Notebook-driven pipelines (Databricks, Vertex)
• agents build experience (planner + tools + memory)
• Tool invocation patterns
• understanding of Prompt/tool separation vs credential access
• Experience with Vault and cloud KMS together (multi cloud environment experience)
• Event-driven automation (Pub/Sub, Event Grid)
• Exposure to AI platform security reviews and implementation
At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position’s starting pay is: $ 95.00/hr.
Location: Mount Laurel, NJ (Hybrid Position 4 days a week)
Duration: 6 Months
Job Description:
We are seeking an AI IAM & Infrastructure Sr. Engineer to be a key technical leader, bridging the gap between cutting-edge AI innovation and our core IAM framework. Your mission will be to drive forward-looking security strategies and engineering solutions for Generative AI and LLM platforms, while specializing in leveraging AI security capabilities to augment and fortify existing enterprise solutions
Key Responsibilities: Research, Evaluation, and Design
This role is primarily focused on providing AI Security/IAM Infrastructure solutions, researching, assisting in designing, and implementing solutions that mitigate gaps in security/IAM controls, and support leadership strategy and road maps. You will be responsible for conducting proof-of-concepts (PoC's) for new security technologies and protocols, and support hardening efforts to protect our mission-critical assets deployed across Azure, Google Cloud, and On-Premises environments.
1. Advanced Protocol and Application Security
Generative AI Protocols: Evaluate and secure emerging standards for multi-agent workflows, such as the Agent-to-Agent (A2A) and Model Context Protocol (MCP).
Threat Modeling: Support threat modeling exercises for new AI applications and pipelines to proactively identify design flaws and adversarial attack vectors (e.g., prompt injection paths).
Mitigation Solutions: Support the design, build, and testing of security controls to mitigate common AI/ML attacks as outlined by frameworks like the OWASP Top 10 for LLM Applications, Mitre Atlas.
2. Access, Identity, and Cloud Controls
IAM Design/implementation: Define and implement security designs for Identity and Access Management (IAM), specializing in securing non-human identities, service principles, and cross-cloud access.
API Security: Own the security strategy for all AI service consumption, including hardening of API Gateways and securing authentication flows (e.g., OAuth 2.0/OIDC) for model endpoints.
Secrets Management: Design and PoC the secure storage, injection, and rotation of confidential data (API keys, model weights, database credentials) using solutions like Azure Key Vault and GCP Secret Manager in support of AI Security Infrastructure initiatives.
AI Cloud Hardening: Establish security configuration baselines, AI IAM framework, and network segmentation (e.g., Private Link, VPC Service Controls) for AI-specific cloud resources on Azure and GCP.
3. Collaboration and Strategy Translation
AI Red Team Support: Provide essential infrastructure security expertise and tooling to support the AI Red Team program, helping them build secure testing environments and validate attack findings.
Translation to Production: Collaborate with IAM, DevOps, Governance, Vulnerability Management, and Platform Engineering partners to translate successful security PoC's and designs into robust, production-ready solutions and Infrastructure as Code (IaC) controls.
MUST-HAVE Hard Skills:
Technical Skills
1.Cloud KMS & Crypto
• KMS concepts (GCP KMS, Azure Key Vault, HSM vs software keys)
• Enveloping encryption patterns
• full Key lifecycle experience: create → rotate → revoke → audit
• IAM bindings at the key / key-ring level (not just project/subscription) & BYOK / CMEK integrations.
2. Non-Human Identity & Workload Identity -huge for agentic AI.
• Service accounts / managed identities
• Workload Identity Federation (OIDC)
• Token exchange flows (STS)
• Least-privilege scoping for automation and agents
3. Policy-as-Code & Guardrails for Low-code development + AI IAM guardrails
• Terraform modules (inputs → opinionated defaults)
• policy engines (OPA, Sentinel, Azure Policy)
• Preventing key misuse via design/AI IAM framework module
4. Low-Code / Integration Fluency/AI
• Azure Data Factory
• Logic Apps / Power Automate
• Notebook-driven pipelines (Databricks, Vertex)
• agents build experience (planner + tools + memory)
• Tool invocation patterns
• understanding of Prompt/tool separation vs credential access
• Experience with Vault and cloud KMS together (multi cloud environment experience)
• Event-driven automation (Pub/Sub, Event Grid)
• Exposure to AI platform security reviews and implementation
Applicant Notices & Disclaimers
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At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position’s starting pay is: $ 95.00/hr.