AI Architect

BrainRidge Consulting; Toronto; In loco; CAD 140.000 - 220.000; Tempo pieno

Descrizione del lavoro

BrainRidge Consulting is a premium FinTech Technology Consulting firm seeking an AI Architect to design enterprise-grade AI platforms and agentic systems for clients. You will drive end-to-end AI architecture, from model selection to deployment, ensuring security, observability, and cost controls in large organizations.

With 7+ years in software/solutions architecture and hands-on experience in Python, cloud platforms, and MLOps, you will collaborate with business and engineering teams to turn

Competenze

• 7+ years in software/solutions architecture with AI systems.
• Experience across generative and agentic AI, LLMs, RAG, multi-agent systems.
• Experience with AI frameworks and model ecosystems listed above.
• Cloud platform experience (Azure, AWS, or Google Cloud).
• Data/knowledge architecture, including databases and knowledge graphs.
• Enterprise software architecture, microservices, REST APIs, Kubernetes, IAM.
• Hands-on with Python, SQL, APIs, Git, CI/CD, Docker, Kubernetes, IaC.
• Familiar with MLOps/LLMOps/AgentOps and observability tools.
• Able to converse with executives and with technical teams across domains.

Mansioni

• Design end-to-end architectures for Generative and Agentic AI solutions.
• Translate business needs into scalable technical architectures.
• Architect AI agents, tools, memory, prompts, and human-in-the-loop controls.
• Define enterprise AI platforms, gateways, registries, and observability.
• Establish guardrails, governance, usage monitoring, and cost controls.
• Evaluate AI models for capability, latency, security, and cost.
• Design retrieval/knowledge architectures across data types.
• Integrate AI with enterprise apps, APIs, and systems of record.
• Define AI security patterns for identity and data protection.
• Lead architecture workshops and proofs of concept; mentor engineers.

Conoscenze

AI system design

Enterprise architecture

Cross-functional leadership

Executive communication

Security & governance

Strumenti

LangChain/LangGraph

LlamaIndex

Semantic Kernel

AutoGen

CrewAI

MCP

OpenAI

Anthropic Claude

Google Cloud

Azure

AWS

Kubernetes

Docker

Infrastructure as Code

Descrizione del lavoro

BrainRidge Consulting is a premium FinTech Technology Consulting firm with the energy of a startup and the experience of an enterprise. With a mission to empower financial institutions and organizations to thrive in the digital age, we provide cutting-edge technology solutions and strategic guidance to drive innovation and growth. As we continue to grow, we are seeking motivated and experienced AI Architects to join our team.

Role Overview

As an AI Architect, you will design and build enterprise-grade AI solutions, platforms, and agentic systems for our clients. This role sits at the intersection of AI, software architecture, cloud, data, security, and enterprise technology, and you'll work with business and engineering teams to turn complex AI use cases into scalable, secure, production-ready solutions. This role is ideal for someone who goes beyond demos and prompt engineering, with a strong understanding of how to architect AI systems that operate reliably inside large enterprises.

Key Responsibilities

• Design end-to-end architectures for Generative AI, Agentic AI, RAG, machine learning, and intelligent automation solutions.
• Translate business requirements into scalable technical architectures and implementation approaches.
• Architect AI agents, including their tools, workflows, memory, permissions, context, and human-in-the-loop controls.
• Design enterprise AI platforms and reusable capabilities, including model gateways and catalogs, agent runtimes and registries, RAG and enterprise knowledge services, prompt and tool registries, and evaluation and observability platforms.
• Define guardrails, governance, usage monitoring, and cost controls for enterprise AI systems.
• Evaluate and recommend AI models based on capability, latency, security, cost, and use-case requirements.
• Design retrieval and knowledge architectures across structured and unstructured enterprise data.
• Integrate AI solutions with enterprise applications, APIs, databases, workflows, and systems of record.
• Define AI security patterns covering identity, authorization, data protection, prompt injection, tool execution, and agent permissions.
• Establish architecture patterns for AI evaluation, observability, testing, monitoring, and production operations.
• Define deployment strategies across cloud, hybrid, containerized, serverless, and Kubernetes environments.
• Develop reusable AI reference architectures, standards, patterns, accelerators, and architecture decision frameworks.
• Lead architecture workshops, technical discovery sessions, and proofs of concept.
• Provide technical leadership and guidance to AI engineers, software engineers, data engineers, and platform teams.
• Partner with security, data, cloud, enterprise architecture, risk, privacy, and business stakeholders.

Qualifications

• 7+ years of experience in software or solutions architecture, including meaningful experience architecting AI or machine learning systems.
• Strong experience across generative and agentic AI, including LLMs, RAG, multi-agent systems, tool/function calling, context engineering, AI memory, and model routing.
• Experience with AI frameworks such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or MCP, and model ecosystems such as OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, or Cohere.
• Strong experience with at least one major cloud platform (Azure, AWS, or Google Cloud) and its AI services.
• Experience with data and knowledge architecture, including SQL/NoSQL databases, data lakes and warehouses, vector databases, hybrid/semantic search, and knowledge graphs.
• Strong understanding of enterprise and software architecture, including distributed systems, microservices, REST APIs, event-driven architecture, Kubernetes, serverless computing, and identity and access management.
• Hands-on experience with Python, SQL, APIs, Git, CI/CD, Docker, Kubernetes, and Infrastructure as Code.
• Familiarity with MLOps/LLMOps/AgentOps practices and observability tools such as MLflow, LangSmith, Langfuse, Arize Phoenix, or Weights & Biases.
• Ability to move comfortably between executive conversations and deep technical discussions.
• Demonstrated experience working across multiple business domains and with cross-functional technical and business teams.