Staff AI Engineer
Role Overview
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, control-flow decisions, and session analytics.
Track record of shipping high-impact features quickly while managing technical debt pragmatically.
Technical Initiative: Proactively proposes new solutions and architectures.
Low-Ego Collaboration: Eagerly adapts initial designs based on team feedback.
What You'll Bring
To be successful in this role, you should have:
Full Job Posting
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, control-flow decisions, and session analytics.
Track record of shipping high-impact features quickly while managing technical debt pragmatically.
Technical Initiative: Proactively proposes new solutions and architectures.
Low-Ego Collaboration: Eagerly adapts initial designs based on team feedback.
What You'll Bring
To be successful in this role, you should have:
Required Qualifications
- Skills & Competencies
- Why Join Securitas
- When you join Securitas, you become part of a global organization committed to your success. We offer:
- Competitive pay
- Weekly pay
- Comprehensive benefits for eligible employees
- Paid training and professional development
- Career advancement opportunities
- Employee recognition programs
- Supportive and inclusive work environment
- Opportunity to work for the world's leading protective services company
- Position: Hybrid (2 days a week) if within commutable distance, or fully remote. Minimal travel required (up to 2 domestic trips per year).
- Compensation Information
- Pay Range: $155,000 - $180,000 annually
- Actual compensation is determined by factors including experience, qualifications, work location, applicable laws, and business needs.
- Eligible employees may also receive benefits including:
- Medical, dental, and vision insurance
- Life and disability insurance
- Paid time off
- 401(k) or retirement savings plan
- Employee assistance program
- Additional voluntary benefits, where applicable
- To be successful in this role, you should have
Responsibilities
- 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, control-flow decisions, and session analytics.
- Track record of shipping high-impact features quickly while managing technical debt pragmatically.
- Technical Initiative: Proactively proposes new solutions and architectures.
- Low-Ego Collaboration: Eagerly adapts initial designs based on team feedback.
About Securitas Security Services
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