Forward Deployed – AI Solutions Architect, GenAI, Agentic AI

This is a fully remote position, open to applicants in Canada.

📋 Description

• Collaborate directly with enterprise clients to comprehend business goals, identify AI possibilities, and convert requirements into scalable GenAI and agentic AI solutions.

• Facilitate technical discovery sessions, architecture workshops, and solution design conversations with client stakeholders and cross-functional teams.

• Create comprehensive AI architectures that include LLM integration, retrieval-augmented generation (RAG), agent orchestration, data processing, APIs, and enterprise system integration.

• Generate solution blueprints, technical specifications, architecture diagrams, and implementation roadmaps that align with client needs and business objectives.

• Deliver technical recommendations, architectural trade-offs, and implementation strategies to technical teams and executive stakeholders.

• Assess third-party and partner-suggested AI solutions for architecture, agent workflows, implementation quality, scalability, and alignment with enterprise needs.

• Design and develop enterprise GenAI applications utilizing LLMs, RAG, embeddings, vector search, prompt engineering, and tool/function invocation.

• Architect and execute AI agents, multi-agent systems, and agentic workflows for intricate, multi-step business processes.

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

• Develop solutions for unstructured data that involve document/PDF extraction, OCR, image processing, classification, information retrieval, and summarization.

• Build and maintain Python services, API integrations, and reusable AI application components.

• Utilize LangChain, LangGraph, or similar technologies for orchestration, state management, and tool-enabled workflows.

• Implement AI-assisted and specification-driven development methodologies while ensuring code quality, testability, and architectural coherence.

• Design and execute AI solutions leveraging Databricks AI capabilities, including AI Functions, AI Search/Vector Search, Model Serving, and MLflow.

• Integrate both structured and unstructured enterprise data, taking into account data quality, access controls, governance, and security.

• Establish patterns for model selection, deployment, inference, retrieval, and enterprise integration.

• Collaborate with data engineering, cloud engineering, and software development teams on platform and production integration.

• Develop 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-related 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 to sustainable enterprise operations.

⛳️ Requirements

• Over 8 years of experience in software engineering, solution architecture, AI/ML engineering, or related technical roles.

• A minimum of 2 years of practical experience in developing Generative AI applications and LLM-driven solutions.

• Proven experience in designing and constructing AI agents, agentic workflows, and multi-agent applications.

• Solid practical understanding of LLMs, RAG architectures, embeddings, vector databases or vector search, prompt engineering, and tool/function invocation.

• Experience in processing unstructured data, including PDFs, documents, OCR outputs, images, and various types of enterprise content.

• Strong Python development capabilities, encompassing API development, integration strategies, debugging, and software engineering best practices.

• Familiarity with AI application frameworks such as LangChain, LangGraph, or comparable agent orchestration technologies.

• Knowledge of Databricks AI capabilities, especially 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 evaluate AI architecture proposals, review code and agent workflows, and provide actionable technical insights.

• Experience with deploying AI applications in production settings, considering scalability, security, monitoring, and cost optimization.

• Excellent communication and presentation skills, able to elucidate technical concepts, trade-offs, and recommendations to enterprise clients.

• Demonstrated ability to collaborate effectively with client stakeholders, technology partners, and multidisciplinary engineering teams.

🏝️ Benefits

• Engaging work environment with opportunities for professional growth.

• Access to cutting-edge technologies and tools in the AI domain.

• Comprehensive health and wellness benefits.

• Flexible working arrangements to support work-life balance.