AI Platform Engineer-6

Job summary

Build and scale an enterprise agentic AI platform that enables development teams to run AI-driven software development lifecycle workflows. Design and operate production-grade agents, tools, skills, and MCP servers across specification, implementation, testing, deployment, and post-deployment operations, using frameworks such as LangChain and LangGraph. Establish secure, machine-checked workflow controls and reusable capabilities that integrate with DevOps and improve engineering productivity.

Qualifications

• Software development experience with Python, TypeScript, or JavaScript.
• Ability to build, deploy, and operate production-grade services.
• Hands-on experience with LangChain, LangGraph, or comparable agent frameworks.
• Practical experience with MCP servers and integrations for AI coding environments.
• Experience using coding assistants such as Claude Code for engineering work.
• Experience developing reusable software development tools and automation.
• Experience integrating AI capabilities into DevOps and software delivery workflows.
• Ability to work across platform, security, and development teams.
• Preferred: Experience with spec-driven development and AI-assisted SDLC.
• Preferred: Experience with managed agent platforms such as Devin.
• Preferred: Knowledge of enterprise DevOps, CI/CD, cloud platforms, and platform engineering.
• Preferred: Experience with governance, security, and automated quality gates for AI-driven workflows.

Responsibilities

• Design and build agents across the software delivery lifecycle, from specification through deployment and post-deployment operations.
• Develop agents using coding harnesses, custom skills, tools, and MCP servers.
• Select platforms, frameworks, and technologies to meet use-case and enterprise requirements.
• Implement and extend spec-driven development workflows.
• Create machine-checked controls for AI-driven lifecycle gates, including specification-ready and pull-request-ready checks.
• Evaluate frontier AI models, coding assistants, agent platforms, frameworks, and architectural patterns.
• Develop reusable AI capabilities and automation for software development teams.
• Partner with DevOps, security, and platform teams to deploy agents, skills, and tools safely in production.
• Maintain shared enterprise skill and tool catalogs.
• Provide build-versus-adopt recommendations for emerging technologies.
• Demonstrate agent capabilities and explain their technical implementation to development teams.
• Guide teams adopting AI-driven development practices and troubleshoot implementation issues.

Skills

Work schedule

Relocation