Forward Deployed – AI Solutions Architect, GenAI, Agentic AI

Job not on LinkedIn

🔥 54 minutes ago

🇨🇦 Canada – Remote

💵 $160k - $190k / year

⏰ Full Time

🟠 Senior

🔴 Lead

💻 Solutions Engineer

👻 Ghost score 24%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ShyftLabs

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

💼 Consulting

🏢 Enterprise

Artificial Intelligence • Consulting • Enterprise

ShyftLabs is a company that specializes in delivering data and AI solutions to enterprises. They offer a range of services, including consulting, prototyping, solutions, platform scaling, and operations to help businesses navigate their data journey and leverage AI for transformative impact. ShyftLabs focuses on empowering organizations with advanced data analytics, AI capabilities, and secure data infrastructure to drive real value and optimize operations across the enterprise. They partner with clients throughout the entire lifecycle of data and AI needs, ensuring efficient and impactful integration of AI solutions into existing workflows. With expertise in cloud platform services and a commitment to secure, optimal performance, ShyftLabs delivers tailored solutions for strategic data and AI transformation.

📋 Description

• 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 technical teams and executive stakeholders • Evaluate third-party and partner-proposed AI solutions for architecture, agent workflows, implementation quality, scalability, and enterprise alignment • Design and build enterprise GenAI applications using LLMs, RAG, embeddings, vector search, prompt engineering, and tool/function calling • Architect and implement AI agents, multi-agent systems, and agentic workflows for complex, multi-step business processes • Develop intelligent workflows integrating LLMs with enterprise APIs, databases, business applications, and external tools • Build unstructured-data solutions for document/PDF extraction, OCR, image processing, classification, information retrieval, and summarization • Develop and maintain Python services, API integrations, and reusable AI application components • Use LangChain, LangGraph, or equivalent technologies for orchestration, state management, and tool-enabled workflows • Apply AI-assisted and specification-driven development practices while maintaining code quality, testability, and architectural consistency • Design and implement AI solutions using Databricks AI capabilities, including AI Functions, AI Search/Vector Search, Model Serving, and MLflow • Integrate structured and unstructured enterprise data while considering data quality, access controls, governance, and security • Define model selection, deployment, inference, retrieval, and enterprise integration patterns • Collaborate with data engineering, cloud engineering, and software development teams on platform and production integration • Establish evaluation strategies for accuracy, relevance, groundedness, hallucination rates, tool-use effectiveness, latency, and cost • Implement guardrails, responsible AI controls, human-in-the-loop workflows, and fallback mechanisms • Define testing and observability practices for agent execution, model behavior, workflow performance, and production issues • Review solution designs and code for quality, maintainability, scalability, security, and operational readiness • Optimize model selection, prompt design, retrieval strategies, inference performance, and operating costs • Support production deployment, troubleshooting, continuous improvement, and transition into sustainable enterprise operations

🎯 Requirements

• 8+ years of overall experience in software engineering, solution architecture, AI/ML engineering, or related technical roles • At least 2 years of hands-on experience developing Generative AI applications and LLM-powered solutions • Demonstrated experience designing and building AI agents, agentic workflows, and multi-agent applications • Strong practical knowledge of LLMs, RAG architectures, embeddings, vector databases or vector search, prompt engineering, and tool/function calling • Experience processing unstructured data, including PDFs, documents, OCR outputs, images, and other enterprise content • Strong Python development skills, including API development, integration patterns, debugging, and software engineering best practices • Experience with AI application frameworks such as LangChain, LangGraph, or equivalent agent orchestration technologies • Familiarity with Databricks AI capabilities, particularly AI Functions, AI Search/Vector Search, Model Serving, and MLflow • Experience with AI evaluation frameworks, hallucination testing, guardrails, human-in-the-loop patterns, and AI observability • Ability to assess AI architecture proposals, review code and agent workflows, and provide actionable technical recommendations • Experience deploying AI applications into production environments, including considerations for scalability, security, monitoring, and cost optimization • Strong communication and presentation skills, with the ability to explain technical concepts, trade-offs, and recommendations to enterprise clients • Proven ability to work collaboratively with client stakeholders, technology partners, and multidisciplinary engineering teams