Ville : Waterloo

Catégorie : PERMANENT FULLTIME

Industrie : Insurance

Employeur : Definity

The AI Platform Architect is responsible for defining, evolving, and governing the enterprise AI platform. This role provides architectural leadership and deep technical expertise to enable scalable, secure, and operationally ready AI capabilities across the organization.

Working closely with technology, data, security, and business partners, the AI Platform Architect translates business requirements into reusable AI platform components, standards, and patterns. The role ensures GenAI capabilities are designed and operated in alignment with enterprise architecture, governance, risk management, and responsible AI principles, enabling multiple delivery teams to build AI solutions consistently and safely.

What to expect 

Arhitect & Platform Strategy 

• Define and maintain the enterprise reference architecture for GenAI platforms, including LLM integration, orchestration, deployment, and evaluation patterns.

• Establish AI architectural standards, design patterns, and decision frameworks to enable consistent GenAI solution delivery.

• Partner with business, data, security, and engineering leaders to translate business needs into scalable, reusable AI platform capabilities.

Gen ai Systems & Design Patterns

• Design and guide implementation of GenAI systems combining non‑deterministic LLM inference with deterministic software, data, and workflow orchestration.

• Standardize and evolve enterprise patterns for:

  • Retrieval‑Augmented Generation (RAG)

  • Prompt lifecycle management

  • Agentic workflows and tool orchestration

  • Model routing, fallback strategies, and cost optimization

• Evaluate and recommend GenAI frameworks and tooling (e.g., LangChain, MCP, A2A, or equivalent).

AI Quality, Evaluation & Observability 

• Define evaluation strategies and frameworks to measure GenAI quality across accuracy, relevance, safety, latency, and cost.

• Embed continuous evaluation, feedback loops, and monitoring into production AI workflows.

• Provide visibility into model performance through dashboards, metrics, and executive‑ready reporting.

Mlops, Llmops & Production Operations 

• Architect and evolve MLOps and LLMOps capabilities, including:

  • CI/CD pipelines for AI workloads

  • Prompt and model versioning

  • Continuous evaluation and monitoring

  • Production observability (logs, traces, metrics, token usage, and cost)

• Ensure AI systems meet enterprise standards for reliability, scalability, and operational support.

Security, Privacy & Responsible ai 

• Integrate security, privacy, and compliance requirements into AI platform design.

• Apply responsible AI principles including guardrails, access controls, auditability, and risk mitigation.

• Ensure appropriate handling of personal and sensitive data across training, inference, and evaluation workflows.

Technical Leadership & Advisory 

• Build strong stakeholder relationships and lead executive‑level discussions on AI strategy and roadmap decisions.

• Translate AI platform strategy into well‑architected, end‑to‑end GenAI solutions aligned to business outcomes.

• Provide advisory support to delivery teams to accelerate adoption while maintaining architectural integrity.

Level of Problem Solving

Operational:

• Support design reviews, production guidance, and platform troubleshooting for AI systems.

Tactical:

• Evaluate architectural trade‑offs across security, cost, scalability, and regulatory constraints.

• Adapt patterns and frameworks as GenAI technologies and business needs evolve.

Strategic

• Design path‑finding AI platform solutions where precedent may be limited and outcomes are probabilistic.

• Shape enterprise AI platform direction and enable long‑term GenAI capability maturity.

What you bring

• Hands‑on experience architecting and operating AI or ML platforms at enterprise scale.

• Strong expertise with cloud‑native architectures, APIs, containerization, and scalable backend systems.

• Experience with at least one major cloud provider (GCP or AWS), with strong preference for cloud‑native AI platforms.

• Deep understanding of:

  • Model and artifact lifecycle management

  • Continuous training, evaluation, and monitoring of AI systems

  • Data access patterns, SDLC, MLOps/LLMOps guardrails, and operating models

• Familiarity with GenAI tooling, orchestration frameworks, and LLM integration patterns.

• Strong technical communication and documentation skills.

• Pragmatic decision‑making with a strong risk‑awareness and ownership mindset.

• Collaborative team player capable of influencing across technology and business domains.

• Bachelor’s or Master’s degree in Computer Science, Technology, or a related discipline.

• 10+ years of combined experience in software engineering, data engineering, and AI/ML. 

• Demonstrated experience architecting and operating production AI platforms at enterprise scale. 

• Experience defining AI operating models, governance frameworks, and platform strategies.

• Certifications: Cloud certifications preferred; hands‑on expertise with GCP or AWS required.