Cybersecurity Software Engineer II | ML & AI (Remote)

Preferred Qualifications

• 1–3 years of relevant software development or engineering experience.
• Experience programming in Python and at least one object-oriented programming language (Java preferred).
• Experience building agentic AI systems — multi-agent orchestration, tool use, and workflow control using frameworks such as LangChain/LangGraph, Google Agent Development Kit (ADK), or similar.
• Experience developing or integrating Model Context Protocol (MCP) servers and clients to expose internal tools and data sources to AI agents.
• Experience building retrieval-augmented generation (RAG) systems — document ingestion and chunking, embeddings, vector search, and grounding responses in enterprise data.
• Experience with AI evaluation and guardrails — building eval harnesses, measuring accuracy and hallucination rates, prompt engineering, and defending against prompt injection and unsafe tool invocation.
• Experience with cloud-native application development on Google Cloud Platform (Vertex AI / Gemini Enterprise Agent Platform, Agent Engine, Cloud Run, BigQuery, Pub/Sub, Secret Manager). Equivalent depth in Azure or AWS considered.
• Experience writing and optimizing SQL queries against relational databases; familiarity with relational, NoSQL, and vector database technologies.
• Experience building and supporting microservice-based architectures and APIs.
• Experience with CI/CD pipelines, DevOps practices, and automated deployment tools.
• Experience with source code management and version control systems (Git preferred).
• Understanding of security frameworks for user and service authentication and authorization, and of handling sensitive data within AI systems (data classification, model endpoint controls, retention).
• Familiarity with security operations concepts — MITRE ATT&CK, cyber threat intelligence, detection engineering, or offensive security — or demonstrated interest in developing it.
• Experience with modern debugging, monitoring, and root cause analysis techniques, including observability and cost monitoring for LLM-based applications.
• Experience creating and executing unit, functional, integration, performance, and automated tests.