Principal AI Engineer

Hiring Manager: Nurit Azar

Talent Acquisition Advisor: Sarah Dinneen

Job Code Level: DAP4

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

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

The Opportunity:

The OpenText Cloud Services organization is seeking a Principal Artificial Intelligence Developer to join the Cloud Platforms & AI Operations team and serve as a technical authority and lead software developer for agentic AI systems and AI/ML Ops.

You will help shape and implement agent-based AI architecture, design and development, take ownership of enterprise-scale agentic AI systems, influence AI technology direction, and operate at the forefront of applied AI and semi-autonomous systems.

This role is responsible for designing and developing a targeted, expanding fleet of interoperable, capable and performant AI agents against a well-defined roadmap that will transform IT Operations and IT Service Management speed, scale and efficiency for OpenText and customers, and for designing, deploying, managing, and supporting modern, scalable and secure AI/ML Ops infrastructure and workflows across public cloud environments.

You will help enable and achieve our vision to be the best Information Management company in the world-where information and AI elevate every individual and organization to be their best.

What the role offers:

• AI Agent Research: Evaluating and incorporating emerging agent frameworks, model capabilities, software patterns and research trends to guide strategic adoption.
• AI Agent Architecture: Defining and refining reference agent architectures, agent design patterns and best practices for agent-based solutions; LLM choices; reasoning, communication, planning, prompting, state, tool orchestration, identity, access, security, observability, memory, evaluation, autonomy.
• AI Agent Design: Translating business needs and information (processes, procedures, roles and responsibilities, rules and constraints, performance measures and thresholds, facts and artifacts) into agent requirements and appropriate agent patterns and flows.
• AI Agent Development: Designing and implementing highly complex software AI agents, primarily using Python, which integrate with enterprise systems, APIs, and data platforms.
• AI Agent Testing: Functional testing, safety testing, agent response evaluation including regression testing, human feedback loops and automated quality metrics.
• AI Agent Deployment: Automating deployment, monitoring, and governance of AI agents in scalable cloud production environments.
• AI Agent Troubleshooting: Quickly and accurately determining root cause of issues affecting AI agent operation, results, performance, safety and security.
• AI Data Acquisition: Acquiring varied data and datasets via SQL and REST API interfaces (MCP Server and other means).
• ML Models: Using and enhancing existing and developing custom machine-learning models to apply data science and AI to address business challenges and opportunities.

• Minimum 2 years of experience in AI agent design and development (ideally on large-scale, end-to-end agentic projects from architecture to deployment)
• Minimum 5 years of experience in Software Engineering, DevOps, SRE or Cloud Engineering.

• Master's degree in Artificial Intelligence or similar field/subject highly desirable.
• Bachelor's degree in computer science, Software Engineering, Information Systems or similar field/subject required.

• Expertise in AI agent development frameworks (Google Agent Development Kit or similar).
• Expertise in AI agent design, development, and deployment (ITSM use cases desirable).
• Strong in programming and scripting skills (Python, Bash, YAML, JSON).
• Proficient with data extraction using SQL and REST APIs.
• Proficient with AI/ML tools such as Vertex AI, SageMaker, or Azure ML.
• Proficient with CI/CD tools (e.g., Gitlab CI/CD, Flux, Argo Workflows) and infrastructure automation (Terraform, Helm and Ansible).
• Proficient with Cloud platforms (AWS, GCP, or Azure) - especially deployment of AI/ML workloads.
• Proficient with Container orchestration (EKS/GKE/AKS, Anthos/Google Distributed Cloud) and model serving frameworks (e.g., VLLM and TorchServe).
• Proficient with MCP Server Protocol (MCP) and MCP Server development.

• Exposure to Graph databases beneficial.
• Exposure to monitoring and logging tools (Prometheus, Grafana, ELK, CloudWatch, etc.).
• Exposure to data management and versioning, model versioning, and experiment tracking methods.
• Exposure to security, compliance, and governance options and solutions in AI/ML environments.