Secure AI Engineer

Role overview

This role is for an engineer who can move between secure implementation, AI workflow design, evaluation, automation, and architecture support. You will help prototype and validate the secure patterns that support client-facing architecture work.

What you will do

• — Prototype secure AI workflows, RAG pipelines, AI gateways, agentic workflows, and governance-aware application patterns.
• — Implement proof-of-concept controls for prompt injection defense, tool-use boundaries, output validation, audit logging, and sensitive-data handling.
• — Support architecture teams with working examples, reference implementations, and implementation-ready technical guidance.
• — Use AI-assisted development tools responsibly in daily delivery while preserving code quality, security, and review discipline.
• — Contribute to evaluation harnesses, test cases, documentation, and reusable implementation patterns.

Qualifications

• — 3+ years of software engineering, cloud engineering, platform engineering, security engineering, or AI application development experience.
• — Hands-on experience with Python, TypeScript, APIs, cloud services, CI/CD, containers, or secure application patterns.
• — Working understanding of LLM applications, RAG, embeddings, agents, tool calling, AI gateways, model APIs, or AI-assisted development.
• — Familiarity with OWASP LLM risks, secure coding, identity, logging, data protection, and threat modeling.

Preferred experience

• — Experience with OpenAI, Azure OpenAI, Anthropic Claude, LangChain, LangGraph, LlamaIndex, vector databases, or model evaluation tooling.
• — Experience with GitHub Copilot, Cursor, Claude Code, Codex, or similar agentic coding workflows.
• — Experience building internal demos, architecture proof assets, or client-ready prototypes.

Additional information