Generative AI Engineer

About the role

Responsibilities

• Design, build, and deploy AI-powered capabilities across the SDLC, including:

Spec Driven Development workflows that support the translation of well-formed specifications into secure, verifiable implementations

Assurance of AI-generated code - guardrails, policy enforcement, and verification for code produced by AI assistants and agents

SDLC skills and agent tooling - developer-assist skills as well as verification skills that perform automated security checks (design review, dependency and supply-chain analysis, static/dynamic analysis orchestration, release audit support)

Integrate solutions with enterprise systems - source control, CI/CD, ticketing, security scanning,

identity, and internal platforms - through APIs, webhooks, and protocols such as MCP (Model Context

Protocol)

Partner with engineering, security, product, and leadership stakeholders to define requirements,

evaluate trade-offs, and support solution adoption

Apply sound architecture and systems design practices: well-defined service boundaries, appropriate

data models, secure defaults, observability, and extensibility

Required Skills and Qualifications

Demonstrated experience developing and deploying AI-based solutions in production environments,

with measurable business or operational impact

Strong programming proficiency (e.g., Python, TypeScript/JavaScript, Go, or similar) and adherence to

software engineering best practices, including testing, code quality, and maintainability

Hands-on experience with modern AI/LLM development, including:

Context engineering - designing what informs the model's context window, including agentic retrieval and search, memory architectures, grounding in enterprise data, and structured outputs

Agentic system design - agent loop engineering, multi-agent and sub-agent orchestration, and tool/function calling

Context window management and token budgeting, including cost and latency optimization for production workloads

Evaluation of AI system quality, reliability, and safety

Solid understanding of software architecture and systems design, including API design, event-driven

patterns, and data modeling for scalability and extensibility

Experience developing and/or deploying applications with large-scale impact (broad user base, high

transaction volume, or organization-wide adoption)

Experience integrating with multiple systems and platforms (REST/GraphQL APIs, CI/CD pipelines,

cloud services, enterprise tooling)

Demonstrated ability to work independently across the full delivery lifecycle - requirements analysis,

solution design, implementation, deployment, and stakeholder engagement - with accountability for

results

Strong communication and collaboration skills, with the ability to convey technical concepts to both

engineering and business audiences

Working knowledge of secure development practices and experience designing solutions that meet

enterprise security and compliance requirements

Preferred Skills and Qualifications

Experience applying AI within a security domain - application security, DevSecOps, code analysis,

threat modeling, firmware security or software supply-chain security

Familiarity with secure-by-design / secure-by-default principles and relevant frameworks (e.g., OWASP,

including the OWASP Top 10 for LLM Applications; NIST SSDF)

Experience with MCP (Model Context Protocol), building agent skills and tools, or extending AI coding

assistants (e.g., Claude Code, GitHub Copilot, Cursor, Devin)

Experience with AI evaluation frameworks, guardrails, prompt/response caching strategies, and LLMOps in production

Bachelor's or master's degree in computer science or a related field, or equivalent practical Security Harness Engineering