Key skills

Prompt Engineering and LLM IntegrationAgent OrchestrationAgentic SecurityMCP Server IntegrationREST and GraphQL API IntegrationKnowledge Base Design and Retrieval-Augmented Generation (RAG)LLM Evaluations, Metrics, and ObservabilityFull-Stack Application DevelopmentCloud Deployment and Infrastructure AWSCloud Deployment and Infrastructure GCPCloud Deployment and Infrastructure AzureApplication Security, Authentication, Authorization, and Secret ManagementPythonGo and TypeScriptAIAgenticProject Management

About this role

Integrate is building a real-time, cross-boundary project management platform for complex government and commercial programs. The Agentic Operations Engineer will build the company’s internal intelligence operating system by developing AI agents, internal tools, automations, and workflows that improve organizational velocity across engineering, product, design, and operations. The role combines full-stack engineering, strategic operational planning, AI tooling governance, and proactive identification of organizational bottlenecks.

Responsibilities:

Build the internal "intelligence OS" of Integrate — the tools, agents, automations, and conventions that compound the team's productivity over time
Organize our repos, docs, and conventions so that AI coding agents route correctly, find the right context, and produce high-quality output
Stand up and maintain internal tooling — chatbots employees use to find answers, CLI tools that support day-to-day workflows, small web apps that smooth over operational friction
Build and own automations around the PR review process — code quality checks, agent-driven first-pass review, security and convention enforcement
Partner with engineering to identify code-quality issues, ambiguous structures, or stale conventions that slow down both humans and agents, and propose fixes
Support the product team as they get closer to the codebase, and the design team as they work more directly in feature branches — pairing, building scaffolding, refining prompting workflows
Evaluate, deploy, and govern bleeding-edge AI tooling: IDE agents, evals, model gateways, MCP servers, on-prem systems
Audit teams proactively. Surface friction. Propose system-level fixes rather than one-off patches
Be the person teammates come to with questions, ideas, and half-built experiments — and the person who leads when no one is asking
Plan three to six months ahead for what an AI-native Integrate looks like, including on-prem and locally-run agent infrastructure for our most security-sensitive workflows
Contribute occasionally directly to the core application codebase when needed