Principal AI Software Architect

OPENTEXT - THE INFORMATION COMPANY

Open Text is a global leader in information management, where innovation, creativity, and collaboration are the key components of our corporate culture. As a member of our team, you will have the opportunity to partner with the most highly regarded companies in the world, tackle complex issues, and contribute to projects that shape the future of digital transformation.

AI-First. Future-Driven. Human-Centered.

At Open Text, AI is at the heart of everything we do—powering innovation, transforming work, and empowering digital knowledge workers. We're hiring talent that AI can't replace to help us shape the future of information management. Join us.

The AI Engineering and Enablement organization leads Open Text's AI innovation strategy, shaping how generative and agentic AI are transformed into real products and customer‑facing solutions. We work across product, engineering, and research to deliver AI capabilities that power intelligent content, secure workflows, and enterprise‑scale automation across Open Text's portfolio.

Our focus goes beyond platforms and tooling to building AI products, experiences, and shared foundations that accelerate innovation while maintaining the trust, security, and governance required in enterprise and regulated environments. From agentic development and orchestration to AI‑powered solutions built on trusted content, we enable Open Text teams and customers to realize the full potential of AI at scale.

As a Principal Software Architect (GenAI Applications and Agentic Systems), you will serve as a senior engineer responsible for designing, building, and extending the agentic runtimes, orchestration flows, and shared GenAI services that form the backbone of our AI platform strategy.

You will lead the implementation of reusable GenAI components such as agent frameworks, RAG pipelines, vector‑augmented retrieval services, and semantic memory. You will work in close partnership with architects and fellow engineers to productionalize fast‑moving incubation efforts, extend orchestration frameworks, and enable scalable agentic solutions that can be reused across products and deployment environments.

This is a hands‑on role for an experienced AI systems engineer who can take ownership of complex capabilities, deliver robust implementations, and contribute directly to the enablement of AI‑powered applications across Open Text.

As a Principal Software Architect (GenAI Applications and Agentic Systems), you will:

• Design and develop production‑ready components for GenAI applications, including agent workflows, tool execution layers, vector search integrations, and memory modules.
• Implement and optimize retrieval‑augmented generation (RAG) pipelines, including embeddings, hybrid retrieval, and contextual grounding.
• Build reusable agent runtimes and orchestration logic, using frameworks like Lang Chain, Lang Graph, CrewAI, or equivalent.
• Participate in the development of multi‑agent patterns, including asynchronous workflows and Agent‑to‑Agent (A2A) coordination.
• Contribute to the integration of the Model Context Protocol (MCP) for standardized agent‑to‑tool and resource interactions.
• Extend and integrate semantic reasoning into agent flows using knowledge graphs or other structured sources.
• Collaborate with architects and cross‑functional teams to translate high‑level designs into modular, maintainable code.
• Support internal enablement by delivering reference implementations, engineering documentation, and code‑level onboarding materials.

• Write clean, modular Python code using FastAPI, with a strong understanding of system integration and runtime performance.
• Apply and adapt frameworks such as Lang Chain, Lang Graph, CrewAI, or other orchestration platforms to enterprise use cases.
• Design agent workflows that incorporate tools, memory, vector search, structured reasoning, and secure execution.
• Understand and implement A2A interaction flows and integrate agents with tools and resources using MCP.
• Build scalable RAG systems, and optimize embedding…