Sr. Manager, AI & Data Science

Open Text Corporation • Waterloo, ON

2d • Onsite

About The Position

The AI Engineering and Enablement organization leads OpenText's AI innovation strategy, turning generative and agentic AI into real products and customer-facing solutions. We build the agentic development and orchestration foundations, AI-powered solutions on trusted content, and shared services that scale across OpenText's portfolio while meeting the trust, security, and governance bar of enterprise and regulated environments.

As Senior Manager, Applied AI and Services, you are the technical lead for building OpenText's agentic AI ecosystem. This covers both the end-to-end agentic solutions we deliver and the shared cloud and application services they run on. You will design and build agents, workflows, and the common building blocks they share (orchestration, model services, evaluation, protocol integrations, reusable components), bringing your own agentic AI experience to bear in shaping how the ecosystem comes together. The role is structured as a tech lead with team leadership scope: you will break down business outcomes into technical deliverables, then lead and coach a small senior team through implementation while staying hands-on in the work yourself. You will also lead the Model and Data Science sub-team on model awareness and EvalOps.

Requirements

• An engineer's mindset: you reach for the IDE before the slide deck and prove approaches in working code.
• Deep AI-lifecycle literacy across prompt design, agent and service architecture, tool integration, evaluation, observability, and post-deployment iteration.
• Strong development discipline: code review standards, testing strategies for non-deterministic systems, prompt and eval versioning, reproducibility.
• Credibility to lead a Model and Data Science team, with the ability to read a model card, design an eval, interpret results, challenge methodology, and communicate tradeoffs clearly to product, security, and executive stakeholders.
• 6+ years of hands-on software engineering experience, with 2+ years building and shipping production GenAI or agentic systems end-to-end.
• Demonstrable track record of leading the design and delivery of agentic AI solutions in an enterprise context: production systems, not prototypes or notebooks.
• Experience as a hands-on technical lead for small senior teams (engineers and/or data scientists).
• Production-grade Python and Java.
• LangGraph for agent and workflow orchestration.
• Prompt engineering and evaluation experience, including designing, running, and acting on offline and online evals.
• Working literacy of MCP (Model Context Protocol) and A2A (Agent-to-Agent) patterns.
• Cloud services architecture and development: a strong software development background, with hands-on experience designing, building, and operating common cloud and application services (model hosting, model services, evaluation, supporting AI infrastructure) on GCP, AWS, or Azure.
• Familiarity with model selection and tradeoffs across hosted and open-weight model families.

Nice To Haves

• Google ADK (Agent Development Kit).
• RAG pipelines, vector databases, and embedding techniques.
• AI gateway patterns (e.g., LiteLLM) for multi-provider routing and cost/latency control; EvalOps tooling such as LangSmith, Langfuse, or Ragas.
• Containerized, cloud-native deployment (Docker/Kubernetes) for AI workloads.
• Data science or applied ML background sufficient to lead a Model and Data Science team on evaluation and model strategy.

Responsibilities

• Lead the hands-on design and delivery of agentic AI solutions end-to-end, from problem framing through production rollout, covering agents, multi-agent workflows, tool integrations, prompts, and evaluations.
• Architect and build the shared agentic AI services these solutions run on: orchestration, model services, evaluation, protocol integrations, and reusable agent components that other OpenText teams also build on.
• Implement multi-agent workflows using LangGraph, with cloud-native deployment of model services and supporting infrastructure.
• Establish evaluation as a platform capability, including shared eval harnesses, datasets, regression tracking, and EvalOps practices used across the organization.
• Own the AI development lifecycle end-to-end: model selection, prompt design, offline and online evaluation, latency and cost profiling, safety review, and rollout.
• Lead the Model and Data Science sub-team on model awareness and evaluation design; set engineering standards and write the reference implementations the broader team builds against.
• Drive integration with shared platform protocols including MCP and A2A, so the ecosystem composes cleanly across the Aviator and platform stack.
• Partner with product, architecture, security, and platform teams to ship production-ready capabilities meeting observability, security, scalability, and responsible-AI bars; represent engineering in design reviews and leadership forums.

Benefits

• thoughtfully designed benefits package that supports your physical, emotional, and financial wellbeing
• compensation programs, including variable and commission compensation opportunities for eligible roles
• vacation entitlement
• paid time off

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed