AI Architect

About Monster Energy:

Forget about blending in. That's not our style. We're the risk takers, the trailblazers, the gamechangers. We're not perfect and we don't pretend to be. We're raw, unfiltered, and a bit unconventional. Our drive is just like our athletes, unrivaled. The power is in your hands to define what success looks like and where you want to take your career. It's not just about what we do, but about who we become along the way. We are much more than a brand here. We are a way of life, a mindset. Join us.

A Day in the Life:

In the position of AI Architect, you will design and own the technical integration layer that connects Monster's artificial intelligence (AI) platforms (Microsoft Copilot and Copilot Studio, Microsoft Foundry and Azure OpenAI, Salesforce Agentforce, SAP Joule, and Workday) to the company's applications and its Microsoft Fabric data estate. Define the reference architecture, integration standards, and security patterns that govern how AI agents reach enterprise data and tools through the Model Context Protocol (MCP), application programming interfaces (APIs), and events; how agents work with each other and with people; and which models (e.g., OpenAI GPT, Anthropic Claude) fit each use case. Build the core connectors and reference implementations that the Sr. AI Solution Engineer and AI Engineer reuse, turning individual AI projects into a governed, scalable enterprise platform.

The Impact You'll Make:

Enterprise AI Reference Architecture & Patterns — Define and maintain the reference architecture for AI across the Microsoft, Salesforce, SAP, and Workday ecosystems; select and standardize proven AI architecture patterns (retrieval-augmented generation, tool calling, multi-agent orchestration, human-in-the-loop, AI gateway and model routing); publish the standards and decision records every AI deployment follows; lead architecture reviews for new AI use cases entering the AI Steering Committee process.

MCP Gateway & Tool Platform — Design, build, and operate the MCP gateway and a governed registry of MCP servers and tools that expose enterprise systems and Microsoft Fabric data to AI agents; manage approvals, versioning, schemas, and tool descriptions so tools are reusable across Copilot Studio, Microsoft Foundry, Agentforce, Joule, Claude, and other MCP-compatible clients.

Agent Identity, Security & Guardrails — Implement least-privilege agent identities and delegated authorization (e.g., Microsoft Entra ID, OAuth), secrets management, and API controls; design defenses against prompt injection, tool poisoning, and data exfiltration aligned with the OWASP Top 10 for LLM applications; partner with Information Security on threat models and AI red-team testing.

Model Strategy & Multi-Agent Orchestration — Set model selection and routing guidance across OpenAI (GPT-5.6 / GPT-6), Anthropic Claude (Opus 5.5, Sonnet 5.5, Haiku 5.5), Google Gemini (3.1 Pro, 3.8 Flash), and open-weight models (e.g., Meta Llama 4) for cost, quality, latency, and data sensitivity; architect how agents hand work to each other and to people using agent frameworks (e.g., LangChain, Semantic Kernel) and emerging agent-to-agent (A2A) standards.

Enterprise System Integrations — Build and maintain secure integrations between AI agents and CRM, ERP, HRIS, finance, and collaboration systems through REST, GraphQL, webhooks, and event-based interfaces, using platforms such as MuleSoft, Workato, Power Platform / Power Automate, Salesforce Flow, and SAP BTP.

Observability, Evaluation & Cost Management — Instrument agents and integrations with logging and telemetry (e.g., Application Insights / Log Analytics); establish automated evaluation and regression tests for agent quality, safety, and tool-call accuracy; monitor latency, reliability, and model and token consumption to manage run cost.

Technical Leadership & Golden Paths — Act as technical lead and design reviewer for the Sr. AI Solution Engineer, AI Engineer, and AI Data Engineer; publish golden-path templates the team reuses; evaluate vendor AI capabilities (e.g., Agentforce, Joule, ClickUp AI, embedded SaaS agents) and recommend build-vs-buy decisions.

Documentation & Governance Evidence — Document integration contracts, the MCP tool catalog, prompts, runbooks, and architecture decisions; supply evidence for AI governance, audit, and regulatory requirements (e.g., NIST AI RMF, ISO/IEC 42001, EU AI Act).

Who You Are:

Prefer a Bachelor's Degree in the field of Computer Science, Software Engineering, Information Systems, Artificial Intelligence, or related.

5+ years of experience in enterprise integration, software, or solution architecture (APIs, event-driven systems, integration platforms)

1-3 years of experience in designing and delivering production AI agent or large language model (LLM) solutions, including MCP-style connectors, tool calling, and at least one enterprise AI agent framework (Copilot Studio, Agentforce, Joule, or Workday AI)

Computer Skills Desired: AI architecture patterns: retrieval-augmented generation (RAG) and agentic RAG, tool / function calling, MCP client-server design, multi-agent orchestration (orchestrator-worker, agent-to-agent), human-in-the-loop, guardrails, AI gateway and model routing, prompt and semantic caching, structured outputs, agent memory, evaluation-driven development (LLMOps)

Models: OpenAI GPT-5.6 / GPT-6; Anthropic Claude Opus 5.5, Sonnet 5.5, Haiku 5.5; Google Gemini 3.1 Pro / 3.8 Flash; open-weight models (e.g., Meta Llama 4); awareness of GPU / CUDA inference trade-offs

AI tools: Microsoft Copilot Studio, Microsoft Foundry (formerly Azure AI Studio) / Azure OpenAI, OpenAI function calling, Claude tool use and MCP, Joule skills, Agentforce prompts; LangChain or Semantic Kernel

Languages: TypeScript/JavaScript, Python, or C#; React familiarity

APIs & integration: REST, GraphQL, webhooks, event systems; MuleSoft, Workato, Power Platform / Power Automate, Salesforce Flow / Agentforce, SAP BTP

Data: Microsoft Fabric / OneLake, Azure SQL, Power BI

DevOps & security: Git, CI/CD, environment configuration, Azure DevOps; Microsoft Entra ID; Application Insights / Log Analytics

Cloud: Azure, Salesforce, SAP BTP, or equivalent

AI-assisted development: Cursor, Claude Code, or GitHub Copilot used daily

Preferred Certifications: Microsoft AI-103: Developing AI Apps and Agents on Azure (replaces the retired AI-102 / Azure AI Engineer Associate)

Microsoft Certified: Agentic AI Business Solutions Architect (AB-100) or Azure Solutions Architect Expert (AZ-305)

Salesforce Platform Developer I or Integration Architect; Microsoft Power Platform Developer Associate

Workday Integration certification (if applicable); SAP BTP / Joule configuration training

Additional Knowledge or Skills to be Successful in this role: Deep working knowledge of current AI architecture patterns and when to use each; ability to turn business processes into secure, reusable agent architecture and set standards other teams adopt; clear communicator of trade-offs to executives and engineers; hands-on technical leader who mentors the Sr. AI Solution Engineer, AI Engineer, and AI Data Engineer.

Monster Energy provides competitive total compensation. The estimated annual salary range for this position is listed below. Actual compensation may vary based on skills, qualifications, experience, and work location.

Pay Range

$150,000 — $200,000 USD

Monster Energy uses AI-powered tools, including Greenhouse’s Talent Matching feature, to help evaluate applications for this role. To learn more see Monster’s privacy policy here Privacy Policy or contact Monster at [email protected].

• For United States applicants:
• Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
• This employer is required to notify all applicants of their rights pursuant to federal employment la