The Opportunity

ShyftLabs is a fast-growing data and technology company partnering with Fortune 500 organizations to deliver innovative, data-driven solutions. We help enterprise clients solve complex business challenges through modern data platforms, cloud technologies, artificial intelligence, and scalable software engineering. We are seeking an experienced

AI Solutions Architect – GenAI & Agentic AI

to lead the design and delivery of enterprise-grade AI solutions for our clients. This is a highly client-facing role that combines deep technical expertise with solution architecture, stakeholder engagement, and hands-on development. You will work closely with enterprise clients, business stakeholders, engineering teams, and technology partners to translate complex business requirements into practical AI architectures and production-ready applications. You will help define technical strategies, architect agentic workflows, evaluate partner solutions, and ensure AI applications meet enterprise standards for scalability, security, reliability, performance, and cost efficiency. The ideal candidate brings robust hands-on experience building GenAI applications and AI agents, along with the ability to communicate complex technical concepts to both technical and non-technical audiences. You should be comfortable moving between client discovery sessions, architecture reviews, code assessments, and production implementation. What You’ll Be Doing

Partner directly with enterprise clients to understand business objectives, identify AI opportunities, and translate requirements into scalable GenAI and agentic AI solutions.

Lead technical discovery sessions, architecture workshops, and solution design discussions with client stakeholders and cross-functional teams.





Design end-to-end AI architectures covering LLM integration, retrieval-augmented generation (RAG), agent orchestration, data processing, APIs, and enterprise system integration.

Develop solution blueprints, technical specifications, architecture diagrams, and implementation roadmaps aligned with client requirements and business outcomes.

Present technical recommendations, architectural trade-offs, and implementation strategies to both technical teams and executive stakeholders.

Evaluate third-party and partner-proposed AI solutions, assessing architecture, agent workflows, implementation quality, scalability, and alignment with enterprise standards.

Design and build enterprise GenAI applications using large language models, retrieval-augmented generation (RAG), embeddings, vector search, prompt engineering, and tool/function calling.

Architect and implement AI agents, multi-agent systems, and agentic workflows capable of executing complex, multi-step business processes.

Develop intelligent workflows that integrate LLMs with enterprise APIs, databases, business applications, and external tools.

Build solutions for unstructured data processing, including document and PDF extraction, OCR, image processing, classification, information retrieval, and summarization.

Develop and maintain high-quality Python services, API integrations,



and reusable components that support reliable AI application development.

Leverage AI development frameworks such as LangChain, LangGraph, or equivalent technologies to implement orchestration, state management, and tool-enabled AI workflows.

Apply AI-assisted and specification-driven development practices to accelerate implementation while maintaining code quality, testability, and architectural consistency.

Design and implement AI solutions using enterprise data platforms, with familiarity across Databricks AI capabilities, including AI Functions, AI Search/Vector Search, Model Serving, and MLflow.

Integrate structured and unstructured enterprise data into AI applications while considering data quality, access controls, governance, and security requirements.

Define appropriate patterns for model selection, deployment, inference, retrieval, and integration with existing enterprise technology ecosystems.

Collaborate with data engineering, cloud engineering, and software development teams to ensure AI solutions integrate effectively with enterprise platforms and production systems.

Establish evaluation strategies to measure AI solution accuracy, relevance, groundedness, hallucination rates, tool-use effectiveness, latency, and cost.

Implement guardrails, responsible AI controls, human-in-the-loop workflows, and fallback mechanisms to support safe and reliable enterprise AI adoption.

Define testing and observability practices for monitoring agent execution, model behavior, workflow performance, and production issues.

Review solution designs and code for quality, maintainability, scalability, security, and operational readiness.<

📌 Forward Deployed (Toronto)
🏢 ShyftLabs
📍 Toronto