Senior Consultant, Strategy, Growth, and Transformation, Identity & Gen AI Engineer

This role focuses on hands-on engineering, integration, and continuous enhancement of AI solutions where identity and access controls are a first-class concern. The engineer will build generative AI solutions with identity, access, and trust engineered in from the start, securing both human and non-human identities and governing how AI agents and GenAI platforms reach data and downstream systems.

What you'd actually do

  1. Build and integrate generative AI solutions, including LLM applications, retrieval-augmented generation, and AI agents, with secure access to data and downstream systems.
  2. Engineer authentication, authorization, and identity controls for AI agents, service accounts, and other non-human identities operating across enterprise and cloud environments.
  3. Develop guardrails for agentic workflows, including scoped permissions, least-privilege access, credential and secrets management, and runtime policy enforcement.
  4. Implement logging, monitoring, and governance that provide traceability and accountability for AI system actions.
  5. Collaborate with IAM, security architecture, and data teams to embed identity controls into GenAI solution delivery and operations.

Skills

Required

  • software engineering experience with Python or a comparable language
  • hands-on experience building, integrating, or deploying generative AI solutions such as large language model (LLM) applications, retrieval-augmented generation (RAG), or AI agents, including use of model APIs, orchestration frameworks, and AI development tools
  • Working knowledge of identity and access management concepts and protocols, including authentication, authorization, single sign-on (SSO), and standards such as OpenID Connect (OIDC), Security Assertion Markup Language (SAML), OAuth, and JSON Web Token (JWT)

Nice to have

  • Experience deploying generative AI solutions to production environments
  • Hands-on experience with identity and access management platforms such as SailPoint, Okta, or Microsoft Entra ID
  • Experience securing non-human or machine identities, service accounts, secrets, and credentials using tools such as HashiCorp Vault or CyberArk
  • Experience with AI agent frameworks and protocols such as LangChain, LangGraph, or Model Context Protocol (MCP)
  • Experience with fine-grained authorization or policy-as-code using tools such as Open Policy Agent (OPA), Cedar, or OpenFGA
  • Familiarity with AI and LLM security risks such as the OWASP Top 10 for LLM Applications, prompt injection, and excessive agency
  • Experience applying AI governance and risk frameworks such as the NIST AI Risk Management Framework (AI RMF)
  • experience building or deploying workloads in cloud environments such as Amazon Web Services (AWS) and Microsoft Azure
  • Certified Information Systems Security Professional (CISSP), Certified Cloud Security Professional (CCSP), or a cloud engineering certification such as AWS Certified Solutions Architect or Microsoft Certified: Azure Solutions Architect
  • experience supporting federal government environments
  • experience with infrastructure-as-code or automation technologies such as Terraform or Ansible

What the JD emphasized

  • identity and access controls are a first-class concern
  • securing how AI agents, models, and automated workflows access enterprise systems and data
  • hands-on engineering, integration, and continuous enhancement of AI solutions
  • identity and access controls are a first-class concern

Other signals

  • identity and access controls are a first-class concern
  • securing how AI agents, models, and automated workflows access enterprise systems and data
  • engineering authentication, authorization, and identity controls for AI agents