# Kavaliro - Solutions Architect AI - JD title: Solutions Architect AI - JD Company: Kavaliro - JD url: https://www.linkedin.com/jobs/view/4453353262 - JD Posting date and time: Estimated 2026-08-12 03:49 PM EDT; derived from LinkedIn public value '4 hours ago' at capture time 2026-08-12 07:49 PM EDT - Number of applicants if available: 25 applicants - Date and time the resume was generated: 2026-08-12 07:49 PM EDT - Elapsed time between JD Posting date and time vs the resume generation time: 4 hours, 0 minutes - JD level: Individual contributor / senior architecture leadership role. The JD focuses on enterprise AI strategy, architecture, governance, scalable AI platforms, and technical delivery frameworks rather than explicit people management. - Compensation: $200,000 - $220,000 base pay range. - Count and percent of matching Significant keywords in JD vs in resume: 24 JD / 21 resume (87.5%) - Fit Notes / caveats: United States role with $200K-$220K base pay range. Travel and remote/hybrid cadence are not disclosed. - JD text: See captured JD below. ## Captured JD We are seeking an experienced AI Architecture leader to define and drive the enterprise AI strategy, architecture, governance, and delivery framework across a complex business environment. This role will be responsible for building scalable AI platforms, establishing architectural standards, and enabling the adoption of Machine Learning, Generative AI, Agentic AI, and advanced analytics solutions that create measurable business value. The ideal candidate combines deep expertise in AI/ML architecture, modern data platforms, cloud technologies, AI governance, observability, and security. This individual will partner closely with business, product, engineering, data, and security leaders to transform business opportunities into production-ready AI solutions while fostering innovation, responsible AI practices, and operational excellence. Key Responsibilities AI Strategy & Architecture Define and execute the enterprise AI architecture strategy aligned with business goals and technology priorities. Develop architectural standards, reference patterns, and best practices for AI, Machine Learning, and Generative AI solutions. Partner with business and technology stakeholders to identify, prioritize, and deliver high-value AI use cases. Establish scalable and reusable AI platforms, services, and frameworks that accelerate innovation and adoption. Drive technology evaluation and architecture decisions across cloud, open-source, and commercial AI ecosystems. AI Platform & Solution Design Architect end-to-end AI solutions from data ingestion and feature engineering through model deployment, monitoring, and integration. Design scalable AI capabilities supporting customer experience, operations, forecasting, automation, recommendations, optimization, and intelligent decision-making. Define integration standards between AI services and enterprise applications, platforms, and data ecosystems. Conduct architecture reviews and ensure solutions meet performance, reliability, scalability, and maintainability requirements. Guide engineering teams through implementation using reusable patterns, frameworks, and reference architectures. Generative AI & Agentic AI Establish standards and best practices for Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, AI agents, and orchestration frameworks. Define approaches for prompt engineering, tool integration, workflow automation, and autonomous AI capabilities. Evaluate emerging technologies and identify opportunities to improve business productivity and customer outcomes through Generative AI. Enable rapid experimentation while ensuring governance, security, and operational readiness. AI Operations & Observability Design and implement enterprise standards for AI monitoring, observability, and operational health. Establish capabilities for model performance monitoring, drift detection, prediction quality measurement, and system reliability tracking. Define KPIs, dashboards, and alerting frameworks to ensure transparency and accountability for production AI systems. Enable continuous learning through feedback loops, automated evaluation, and performance optimization. Partner with platform and engineering teams to embed operational excellence into all AI deployments. AI Security, Governance & Responsible AI Establish enterprise policies and controls governing AI development, deployment, and usage. Define security standards for models, training data, APIs, and AI-powered applications. Assess and mitigate AI-specific risks including adversarial attacks, data privacy concerns, model misuse, and third-party dependencies. Ensure compliance with regulatory requirements, security policies, and responsible AI principles. Partner with Security, Legal, Risk, and Compliance teams to maintain effective AI governance practices. Implement guardrails, content moderation, human oversight mechanisms, and ethical AI controls where appropriate. AI Engineering Excellence Lead adoption of modern MLOps, DevOps, and automation practices for AI delivery. Establish standards for model lifecycle management, CI/CD pipelines, automated testing, deployment automation, and cost optimization. Drive best practices in software engineering, architecture documentation, experimentation, and reusable platform development. Evaluate build-versus-buy decisions and support vendor assessment and technology selection processes. Promote scalable, maintainable, and enterprise-ready architecture patterns. Leadership & Collaboration Serve as a trusted advisor to executives, product leaders, engineering teams, and business stakeholders. Communicate complex technical concepts effectively to both technical and non-technical audiences. Mentor architects, engineers, and data scientists on AI architecture, engineering best practices, and emerging technologies. Foster a culture of innovation, accountability, collaboration, and continuous learning. Drive cross-functional alignment across business, technology, data, and operational teams. Required Qualifications 10+ years of experience in software engineering, data engineering, AI/ML engineering, or architecture roles. 5+ years of experience designing and delivering enterprise-scale AI and Machine Learning solutions. Strong expertise in AI architecture, Generative AI, Machine Learning platforms, and modern data ecosystems. Hands-on experience with Python and leading AI/ML frameworks and tools. Deep understanding of model deployment, MLOps, monitoring, observability, and AI operations. Experience designing AI solutions within cloud-based environments and modern data platforms. Strong knowledge of AI security, governance, privacy, and responsible AI practices. Experience evaluating and implementing both open-source and commercial AI technologies. Proven ability to translate business challenges into scalable technical solutions. Exceptional communication, presentation, and stakeholder management skills. Preferred Qualifications Experience building enterprise AI platforms, Centers of Excellence, or organization-wide AI programs. Expertise in Generative AI, LLMs, RAG architectures, vector databases, and agent-based systems. Experience supporting customer-facing, operational, supply chain, digital, or analytics-driven business environments. Contributions to AI communities, open-source projects, technical publications, or thought leadership initiatives. Cloud certifications and advanced AI/ML certifications. Experience leading large-scale AI transformation initiatives in complex enterprise organizations. Show more Show less Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries Retail Referrals increase your chances of interviewing at Kavaliro by 2x See who you know