Staff AI Engineer
Company: Securitas Security Services USA, Inc.
Location: Elmsford, NY (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-10-16
About this role
About The Role
We are seeking a Staff AI Engineer to lead hands-on development for our enterprise Intelligence platform, which hosts the agents and APIs powering our mobile and web applications. In this role, you will drive the architecture of our AI ecosystem, integrate third-party vendor agents, and mentor engineers across internal and external teams.
What You’ll Do
In this role, you will:
- Drive Delivery: Lead hands-on development for our Agentic AI platform, prioritizing zero-trust security, scalability, and performance in a cloud-native Azure environment. For example, build and deploy new agents that connect with third-party enterprise vendors (e.g., workforce management, HR platforms) to unlock new advanced capabilities.
- Define Architecture: Design secure, reusable agent templates, orchestration workflows, and Model Context Protocol (MCP) and Agent 2 Agent (A2A) connectors.
- Set Engineering Standards: Establish strict Bring Your Own Agent (BYOA) standards, automated testing and CI/CD release workflows in partnership with QA and DevOps.
- Mentor & Elevate: Guide and code-review internal engineers and contractor teams to maintain technical excellence and enforce AI security best practices.
- Integrate AI Tools: Leverage GenAI and developer automation tools to optimize workflows and speed up execution.
- At least 8 years of software engineering experience, with deep proficiency in Python and modern API development
- Proven success designing, deploying, and observing LLM-powered applications, orchestrated multi-agent workflow using LangGraph, and scalable RAG systems.
- Hands-on experience building within cloud-native Azure architectures (e.g., Azure AI Foundry, Container Apps, Cosmos DB, Key Vault).
- Experience building robust API integrations, external system connectors, or Model Context Protocol (MCP) and Agent 2 Agent (A2A) connectors.
- Strong understanding of AI observability tools (e.g., Langfuse) for tracking latency, token usage,...