AI Engineer

Insight Global is seeking a Software Engineer specializing in AI Developer Experience for a leading enterprise client. This engineer will build secure, scalable agentic capabilities that improve developer productivity and accelerate software delivery. The role will focus on multi-agent workflows, MCP servers, enterprise integrations, internal developer tooling, and the creation of an ADX hub through which engineering teams can discover and adopt reusable agents and workflows. The ideal candidate combines strong software engineering fundamentals with hands-on agentic AI experience and understands how to operate AI solutions with appropriate security, governance, observability, evaluation, cost management, and human oversight.

• Hands-on experience designing and implementing agentic systems or multi-agent workflows
• Production experience with Claude Code, AWS Bedrock, AWS AgentCore, CrewAI, LangGraph, or comparable agent frameworks
• Strong TypeScript, Node.js, and React development experience
• Cloud-native application development experience on AWS
• Experience building Model Context Protocol servers or comparable enterprise agent integration patterns
• Experience integrating AI agents with Jira, Confluence, GitHub, internal APIs, or other enterprise systems
• Experience developing developer productivity platforms, custom agent harnesses, CLI tools, or SDLC automation
• Ability to identify SDLC activities that can be safely and effectively augmented with agentic tools
• Experience moving AI concepts from experimentation into secure, governed, observable, and production-ready solutions
• Understanding of AI security, governance, prompt engineering, evaluation, and human-in-the-loop controls
• Experience operating production AI solutions with monitoring, telemetry, cost controls, and operational safeguards
• Ability to explain architecture choices, engineering tradeoffs, risks, and measurable outcomes
• Strong ownership, curiosity, accountability, collaboration, and thought leadership
• Python experience
• Experience creating reusable agent registries, catalogs, packages, plug-ins, or workflow templates
• Experience versioning and scaling agents across multiple engineering teams
• Knowledge of preventing configuration or workflow drift between teams
• Experience building internal developer platforms
• Experience with enterprise AI governance and approval processes
• Familiarity with agent evaluation frameworks and quality benchmarks
• Experience measuring the effect of agents on engineering productivity
• Understanding of SRE, observability, or AI-Ops use cases
• Experience identifying how automation changes or shifts delivery bottlenecks
• Product management or product knowledge within engineering organizations