Job Description
Job Title
GenAI Architect
Location-Toronto- Canada / Hybrid
Experience
10+ years overall IT experience, with 3–5+ years in AI/ML and Generative AI architectures
Role Summary
We are seeking an experienced Generative AI (GenAI) Architect to design, govern, and scale enterprise-grade GenAI solutions. The ideal candidate will be responsible for defining GenAI architecture patterns, selecting appropriate models and platforms, ensuring security and compliance, and enabling business teams to derive measurable value from Generative AI use cases.
This role requires deep expertise across LLMs, cloud platforms, AI frameworks, MLOps, and responsible AI practices, with the ability to translate business problems into robust, production-ready GenAI architectures.
Key Responsibilities
GenAI Architecture & Design
• Define end-to-end GenAI solution architectures, including model selection, orchestration, data pipelines, and integration layers.
• Design scalable patterns for RAG (Retrieval Augmented Generation), agents, fine-tuning, and prompt engineering.
• Establish reference architectures, reusable components, and best practices for GenAI adoption.
Platform & Model Strategy
• Evaluate and select LLMs and GenAI platforms (open-source and commercial) based on cost, performance, security, and regulatory needs.
• Define hybrid architectures leveraging cloud-hosted and on-prem GenAI models where required.
• Guide decisions on fine-tuning vs prompt-based approaches.
Engineering & Integration
• Work closely with engineering teams to integrate GenAI solutions into enterprise systems (APIs, microservices, workflows).
• Ensure robust integration with data platforms, vector databases, and enterprise applications.
• Provide architectural oversight during development and deployment.
MLOps, Security & Governance
• Define MLOps / LLMOps standards for model versioning, monitoring, observability, and lifecycle management.
• Ensure compliance with data privacy, security, and Responsible AI principles.
• Collaborate with legal, security, and compliance teams on AI governance frameworks.
Stakeholder Engagement
• Partner with business leaders, product teams, and clients to identify high-value GenAI use cases.
• Act as a trusted advisor, translating technical capabilities into business outcomes.
• Mentor teams on GenAI concepts, tools, and architectural thinking.
Key Technologies & Frameworks
Large Language Models (LLMs)
• OpenAI / Azure OpenAI (GPT-4.x)
• Anthropic Claude
• Open-source LLMs (LLaMA, Mistral)
GenAI Frameworks
• LangChain
• LlamaIndex
• Semantic Kernel
Vector Databases & Retrieval
• Azure AI Search
• Pinecone / FAISS
Cloud & AI Platforms
• Microsoft Azure (Azure AI, Azure OpenAI)
• Kubernetes (for scalable AI workloads)
Programming & Integration
• Python
• REST APIs / Microservices
MLOps & Governance
• MLflow (or equivalent)
• Responsible AI, data privacy, and security frameworks
Required Qualifications
• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
• 10+ years of experience in solution or enterprise architecture.
• Hands-on experience designing and deploying Generative AI solutions in production.
• Strong understanding of cloud architecture, data platforms, and AI lifecycle management.
• Proven ability to communicate complex technical concepts to senior stakeholde
GenAI Architect
Location-Toronto- Canada / Hybrid
Experience
10+ years overall IT experience, with 3–5+ years in AI/ML and Generative AI architectures
Role Summary
We are seeking an experienced Generative AI (GenAI) Architect to design, govern, and scale enterprise-grade GenAI solutions. The ideal candidate will be responsible for defining GenAI architecture patterns, selecting appropriate models and platforms, ensuring security and compliance, and enabling business teams to derive measurable value from Generative AI use cases.
This role requires deep expertise across LLMs, cloud platforms, AI frameworks, MLOps, and responsible AI practices, with the ability to translate business problems into robust, production-ready GenAI architectures.
Key Responsibilities
GenAI Architecture & Design
• Define end-to-end GenAI solution architectures, including model selection, orchestration, data pipelines, and integration layers.
• Design scalable patterns for RAG (Retrieval Augmented Generation), agents, fine-tuning, and prompt engineering.
• Establish reference architectures, reusable components, and best practices for GenAI adoption.
Platform & Model Strategy
• Evaluate and select LLMs and GenAI platforms (open-source and commercial) based on cost, performance, security, and regulatory needs.
• Define hybrid architectures leveraging cloud-hosted and on-prem GenAI models where required.
• Guide decisions on fine-tuning vs prompt-based approaches.
Engineering & Integration
• Work closely with engineering teams to integrate GenAI solutions into enterprise systems (APIs, microservices, workflows).
• Ensure robust integration with data platforms, vector databases, and enterprise applications.
• Provide architectural oversight during development and deployment.
MLOps, Security & Governance
• Define MLOps / LLMOps standards for model versioning, monitoring, observability, and lifecycle management.
• Ensure compliance with data privacy, security, and Responsible AI principles.
• Collaborate with legal, security, and compliance teams on AI governance frameworks.
Stakeholder Engagement
• Partner with business leaders, product teams, and clients to identify high-value GenAI use cases.
• Act as a trusted advisor, translating technical capabilities into business outcomes.
• Mentor teams on GenAI concepts, tools, and architectural thinking.
Key Technologies & Frameworks
Large Language Models (LLMs)
• OpenAI / Azure OpenAI (GPT-4.x)
• Anthropic Claude
• Open-source LLMs (LLaMA, Mistral)
GenAI Frameworks
• LangChain
• LlamaIndex
• Semantic Kernel
Vector Databases & Retrieval
• Azure AI Search
• Pinecone / FAISS
Cloud & AI Platforms
• Microsoft Azure (Azure AI, Azure OpenAI)
• Kubernetes (for scalable AI workloads)
Programming & Integration
• Python
• REST APIs / Microservices
MLOps & Governance
• MLflow (or equivalent)
• Responsible AI, data privacy, and security frameworks
Required Qualifications
• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
• 10+ years of experience in solution or enterprise architecture.
• Hands-on experience designing and deploying Generative AI solutions in production.
• Strong understanding of cloud architecture, data platforms, and AI lifecycle management.
• Proven ability to communicate complex technical concepts to senior stakeholde
💡 Quick Summary
Seeking a career-building opportunity? The GenAI Architect position is now open for candidates interested in the Architect / Interior Designer Jobs sector. This role in Toronto offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Architect / Interior Designer Jobs is a plus.
