We are Generac, a leading energy technology company committed to powering a smarter world.

Over the 60 plus years of Generac’s history, we’ve been dedicated to energy innovation. From creating the home standby generator market category, to our current evolution into an energy technology solutions company, we continue to push new boundaries.

Major Responsibilities

•  Defines secure reference architectures for LLM, RAG, agentic, and other AI enabled systems across cloud and enterprise environments

•  Leads AI threat modeling and identifies design controls for prompt injection, insecure output handling, excessive agent permissions, model abuse, data leakage, and supply chain risk

•  Establishes architecture standards for identity, secrets management, data classification, network boundaries, logging, model access, and third party AI service consumption.

•  Designs guardrails and security patterns for input filtering, grounding controls, policy enforcement, human in the loop checkpoints, and runtime monitoring

•  Partners with engineering teams to embed security requirements into AI platforms, developer workflows, CI/CD pipelines, and production deployment patterns.

•  Drives evaluation of AI security tooling, including posture management, red teaming, model and prompt testing, observability, and policy enforcement capabilities

•  Creates implementation guidance, reference patterns, and design reviews that help teams deliver secure AI features consistently at scale

•  Supports incident readiness by defining telemetry, detections, escalation points, and response considerations for AI specific security events

•  Influences enterprise AI governance by connecting architecture decisions to risk, compliance, privacy, and acceptable use expectations

Minimum Job Requirements

Education

• Bachelor’s degree in Cybersecurity, Computer Science, Information Security, Software Engineering, Computer Engineering, or a related technical field

Certification / License

Work Experience

•  7 years of experience in cybersecurity, software security, cloud security, or security architecture roles

•  2 years of direct experience designing or securing AI, ML, LLM, or data intensive platforms in production environments

•  Hands on experience with cloud native architecture patterns in AWS, Azure, or GCP, including IAM, logging, networking, encryption, and secrets management

•  Experience performing threat modeling, architecture review, and security design for modern applications, APIs, and distributed systems

Knowledge / Skills / Abilities

• Strong understanding of AI system components, including models, prompts, retrieval layers, agents, plugins, orchestration frameworks, and data pipelines.
• Working knowledge of AI security risks such as prompt injection, insecure tool use, excessive autonomy, training data exposure, sensitive data leakage, and model supply chain concerns.
• Strong grasp of core security architecture domains, including identity, cryptography, application security, cloud security, logging, monitoring, and incident response.
• Clear communicator who can work across engineering, product, privacy, legal, and executive stakeholders
• Ability to translate security risk into practical technical requirements and influence engineers, architects, and platform owners.

Preferred Job Requirements

Education

• Bachelor’s or Master’s degree in Cybersecurity, Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical discipline.

Certification / License

• Cloud security, security architecture, or AI/ML related certifications are helpful but not required

Work Experience

•  Experience securing AI platforms built on services such as AWS Bedrock, Anthropic, Azure OpenAI, Microsoft Azure AI, Google Vertex AI, or equivalent

•  Experience with AI security testing, red teaming, prompt attack evaluation, or model and agent assessment techniques

•  Familiarity with orchestration and agent frameworks

•  Experience implementing policy, observability, and runtime control layers around AI systems and external tools

•  Exposure to governance frameworks such as NIST AI RMF, OWASP Top 10 for LLM Applications, MITRE ATLAS, or similar guidance

Knowledge / Skills / Abilities

• Ability to balance secure by design principles with speed of delivery, and to convert emerging AI risks into practical standards, reusable patterns, and engineering guidance