AI Platform Architect will lead the design and implementation of an enterprise-scale AI Agentic Platform that transforms Quality Engineering and Software Delivery. This role owns the end-to-end technical architecture, establishes engineering standards, and mentors the AI Platform Engineering team. The architect will work closely with Enterprise Architecture, Cloud Engineering, Security, DevOps, Application Teams, and Quality Engineering to deliver a scalable, secure, and reusable AI platform capable of supporting hundreds of enterprise applications. Required Qualifications

- 12+ years in software engineering or platform engineering.
- 5+ years designing enterprise cloud-native platforms.
- 3+ years with Generative AI, LLMs, or AI agent frameworks.
- Expert-level Python development.
- Solid experience with Kubernetes, Docker, APIs, microservices, and cloud platforms (AWS/Azure).
- Hands-on experience with RAG, vector databases, MCP, prompt engineering, and agent orchestration.




- Define the vision and roadmap for the Enterprise AI Platform.
- Design a scalable multi-agent architecture supporting SDLC and Quality Engineering use cases.
- Establish standards for AI agent development, orchestration, prompt engineering, and governance.
- Evaluate emerging AI technologies, LLMs, and agent frameworks.
- Design agent orchestration using frameworks such as LangGraph, Semantic Kernel, or similar.
- Define the Model Context Protocol (MCP) architecture and enterprise connector framework.
- Design RAG, vector database, knowledge graph, memory, and context management strategies.
- Build reusable SDKs, APIs, templates, and reference implementations.
- Define integration patterns for Jira, Azure DevOps, GitHub, Confluence, ServiceNow, cloud platforms, and enterprise APIs. • Establish event-driven communication patterns and secure authentication mechanisms.

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🏢 ValueLabs
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