Aws Ai Solution Architect, On/Qc (Canada)

AWS AI Solution Architect, ON/QC (Canada)
We are seeking an experienced
AWS AI Solution Architect
to join our team and work on the design and implementation of production-grade AI and agentic systems. In this role, you will be responsible for defining AI solution architecture, designing multi-agent workflows, and working closely with Software Engineers, Data Engineers, DevOps Engineers, and customer stakeholders to deliver scalable, secure, and reliable AI solutions on AWS.You will work extensively with Amazon Bedrock, LLM-based applications, agentic frameworks such as LangGraph and LangChain, and modern AWS services used to build and operate enterprise AI platforms.Engagement:
long-term, full-time.
Location:
Canada (ON/QC - ideally). !!! Readiness for business trips to Montreal in the nearest time!What you will be doing:
Design and implement production-grade AI and agentic architectures based on business and technical requirements.
Develop LLM-powered applications, autonomous agents, and multi-agent workflows using frameworks such as LangGraph and LangChain.
Design agent orchestration patterns, including tool usage, routing, planning, memory, state management, human-in-the-loop workflows, and multi-agent collaboration.
Build AI solutions using Amazon Bedrock, including foundation models, Knowledge Bases, Guardrails, model evaluation, and other Bedrock capabilities.
Design and implement RAG architectures, including document ingestion, chunking, embeddings, vector search, retrieval strategies, reranking, and context management.
Integrate AI agents with enterprise systems, APIs, databases, SaaS platforms, and internal tools.
Define architecture for scalable and secure AI workloads across application, data, model, and infrastructure layers.
Participate in hands-on implementation, prototyping, troubleshooting, and technical validation of AI solutions.
Evaluate different foundation models and AI approaches based on quality, latency, scalability, security, and cost requirements.
Establish patterns for observability, tracing, evaluation, testing, and monitoring of LLM and agentic applications.
Define security controls for AI systems, including IAM, data isolation, encryption, PII handling, prompt injection protection, and access control.
Collaborate with Software Engineers, DevOps Engineers, Data Engineers, QA Engineers, and business stakeholders throughout the delivery lifecycle.
Prepare and maintain architecture diagrams, technical documentation, implementation guidelines, and technical decision records.
Support production readiness, performance optimization, troubleshooting, and continuous improvement of deployed AI solutions.
A successful candidate will have:
3+ years of experience in AI/ML, software architecture, solution architecture, or similar technical roles.
Strong hands-on experience designing and implementing LLM-based applications.
Practical experience building AI agents or agentic workflows.
Strong experience with Amazon Bedrock and AWS-based AI architectures.
Experience with agentic and LLM frameworks such as:LangGraph;
LangChain.
Strong understanding of modern LLM application patterns, including:Retrieval-Augmented Generation (RAG);
tool/function calling;
agent orchestration;
multi-agent systems;
memory and state management;
structured outputs;
human-in-the-loop workflows.
Strong understanding of prompt engineering and context management.
Experience integrating LLM applications with external APIs, databases, and enterprise systems.
Good understanding of vector databases, embeddings, semantic search, and retrieval techniques.
Strong understanding of cloud architecture principles, including scalability, high availability, security, observability, and cost optimization.
Experience designing APIs and backend services for AI applications.
Understanding of AI application evaluation, including model quality, hallucination reduction, retrieval quality, latency, and cost.
Ability to translate business requirements into practical AI architecture and implementation decisions.
Strong communication skills and the ability to explain complex AI concepts to both technical and non-technical stakeholders.
Will be a plus:
Experience with AWS AgentCore and its capabilities for building and operating AI agents.
Experience with AWS services commonly used in AI platforms, such as:Lambda/Step Functions;
ECS / EKS;
API Gateway;
DynamoDB;
Aurora / RDS.
Experience with Amazon OpenSearch Service, pgvector, Pinecone, or other vector databases.
Experience implementing MCP-based integrations or similar standardized tool integration approaches.
Experience with LLM observability and evaluation platforms or frameworks.
Experience designing AI solutions that handle sensitive or regulated data.
Understanding of MLOps / LLMOps practices and production AI lifecycle management.
Experience with Python and modern AI application development frameworks.
Experience designing distributed or event-driven architectures.
Experience with additional cloud AI platforms such as Azure AI Foundry or Google Vertex AI.
Working conditions:
16 days of paid time off.Official public holidays.Participation in company events.Health insurance.Engaging projects with opportunities for career growth.Certifications, Udemy courses, Claude/other tools needed.Corporate events, team-building activities.Gifts and recognition from the company.A friendly, open, and supportive team culture without excessive bureaucracy.
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