Solutions Engineer

• Partner with business leaders and teams to identify productivity, automation, and knowledge-management opportunities
• Conduct interviews, workshops, and assessments to gather and document AI use cases
• Evaluate opportunities based on business impact, feasibility, risk, and organizational readiness
• Develop business cases and recommendations for prioritization and investment
• Collaborate with technology leadership to maintain an enterprise AI opportunity roadmap
• Serve as a subject matter expert and internal advocate for Generative AI and agentic technologies
• Drive adoption of enterprise AI platforms and tools, including solutions such as Codex, Claude for Enterprise, Microsoft Copilot, and other approved AI technologies
• Design and deliver training programs, workshops, office hours, and educational materials
• Coach employees and teams on effective prompting, workflow design, and responsible AI usage
• Establish best practices and reusable patterns that enable teams to gain value from AI tools quickly and safely
• Configure, integrate, and support AI-powered workflows and solutions under the guidance of technology leadership
• Assist with implementation of AI agents, automations, and integrations using approved enterprise platforms
• Collaborate with engineering, infrastructure, security, and data teams to ensure solutions meet technical and compliance requirements
• Perform lightweight administration, configuration, testing, and operational support for AI platforms and related services
• Document solution architectures, implementation approaches, and operational procedures
• Promote responsible, secure, and compliant use of AI technologies across the organization
• Partner with security, legal, compliance, and technology teams to ensure appropriate governance practices are followed
• Monitor emerging AI technologies, industry trends, and best practices to inform enterprise strategy
• Help establish standards, metrics, and success measures for AI adoption and business value realization

AI Solutions Engineer

• Lead solution design and architecture for web, mobile, and cloud systems (greenfield and modernization)
• Conduct technical discovery: stakeholder interviews, requirements and constraints gathering, risk analysis, and effort estimation
• Produce architecture artifacts (e.g., C4/UML diagrams, sequence/data flows, ADRs) and collaborate on SOWs, proposals, and technical roadmaps
• Support the sales team on technical calls, RFPs, scoping, and demos, translating business goals into actionable engineering plans
• Mentor and upskill engineers through design/code reviews, pairing, and architectural coaching
• Contribute hands-on to projects as needed—pushing code, debugging complex issues, and unblocking teams
• Assist complex client projects even when not directly assigned, advising on scalability, performance, integrations, or architecture challenges.
• Define reference architectures, coding standards, and CI/CD/IaC patterns; champion reliability, observability, and cost optimization
• Lead evaluations of new technologies and frameworks; build PoCs/spikes to validate technical assumptions
• Ensure non-functional requirements (availability, latency, compliance, security) are met and measurable
• Document and formalize the Solutions Architect role at AgilityIO — creating guidelines, success metrics, and repeatable frameworks for future architects

Solutions Architect

• Own the end-to-end AI architecture across LLM orchestration, retrieval, enterprise data integration, and deployment.
• Define architectural standards, best practices, and reusable AI patterns across the platform.
• Evaluate architectural trade-offs involving latency, cost, model quality, scalability, security, and maintainability.
• Define the long-term AI strategy in collaboration with the Delivery Lead and engineering leadership.
• Design intelligent agent architectures, including tool calling, retrieval, planning, multi-step reasoning, and state management.
• Design AI integrations with enterprise data platforms, semantic models, APIs, and knowledge repositories.
• Define AI evaluation strategies covering quality, hallucination detection, grounding, latency, and cost optimization.
• Design observability and monitoring capabilities for AI applications, including prompt performance, model behavior, and operational telemetry.
• Collaborate with cloud infrastructure and security teams to ensure secure, resilient, and compliant deployments.
• Contribute directly to proof-of-concepts and critical implementation efforts when needed.

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AI Solutions Engineer

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AI Solutions Architect