Apply Digital - Solution Architect, Agentic Engineering
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Requirements
• Define, own, and evolve the high-level technical architecture for internal and client platforms and shared services, ensuring systems are scalable, secure, observable, and production-ready. • Organizing, distributing and translating backlog requirements from Product, UX and other disciplines, into detailed spec driven requirements for Agents to implement. • Synthesize user stories, site maps, content strategy, components and design systems, brand strategy, etc, into specifications that coding Agents will implement. • Designing and implementing digital solutions that have proper SEO/GEO, accessibility, performance and content management integrations. • Integrations with key Composable platforms such as Contentful, Contentstack, Algolia, Cloudinary, etc. • Champion spec-driven development as a core practice to improve clarity, quality, and predictability across teams. • Design and guide layered and distributed architectures, ensuring sound use of queues, caching, APIs, and database schemas while using teams of coding agents. • Establish standards for creating and consuming RESTful APIs across services. • Coach engineers on effective agent design, prompt architecture, and decision modeling. • Translate business goals into agent capabilities and system-level behaviours. • Define when autonomy is appropriate vs. when human oversight is required. • Lead the design and implementation of AI-powered systems, including: LLM integrations (Gemini 3, Claude Opus, GPT-4.x/5.x); Vector stores and Retrieval-Augmented Generation (RAG) pipelines. • Set direction for agent observability, debugging, and reliability in production environments. • Embed AI coding agents (Copilot, Claude Code, etc.) into development workflows to improve velocity, quality, and developer experience. • Continuously evaluate and introduce new AI tools and techniques to accelerate delivery while maintaining enterprise standards. • Promote disciplined experimentation and learning around emerging AI capabilities. • Provide technical leadership for systems built on Google Cloud Platform (GCP). • Oversee infrastructure design and provisioning, with Terraform as a preferred approach. • Ensure systems meet expectations for reliability, scalability, and operational excellence. • Lead and mentor senior engineers and technical leads across multiple teams. • Foster a calm, supportive, and solution-oriented engineering culture. • Partner with Engineering Managers, Product, and Delivery leaders to align technical initiatives with organizational goals. • Manage dependencies and technical risks across concurrent initiatives. • Identify and mitigate technical, architectural, and delivery risks early. • Drive continuous improvement in how software is designed, built, tested, and operated, especially through AI-enabled practices. • Ensure systems and teams are prepared for long-term maintainability and evolution. • Proven experience in a Technology Director, Principal Engineer, or equivalent senior technical leadership role. • Strong background in modern software engineering and system architecture, including distributed systems. • Demonstrated experience with spec-driven development. • Hands-on experience using AI coding agents (e.g., GitHub Copilot, Claude Code). • Strong prompt engineering skills. • Experience building or integrating systems using LLMs (Gemini, Claude, GPT-4.x/5.x). • Experience implementing vector stores and RAG architectures. • Experience developing or operating AI agents using Agent Development Kits (e.g., Google ADK). • You are a strong polyglot, in particular with Python 3 and TypeScript. • Experience designing and consuming RESTful APIs. • Strong experience with Google Cloud Platform (GCP). • Hands-on experience with Vertex AI and Google Gen AI APIs. • Deep understanding of system design fundamentals (queues, caching, databases, APIs). • Proven ability to mentor senior engineers and lead multiple teams through complex technical initiatives. • Excellent written and verbal communication skills. • Experience with the BMAD Method or similar spec-driven development frameworks. • Experience with AI agent design patterns, task planning, and reasoning. • Experience with agent observability and debugging in production. • Experience with layered and distributed architecture patterns at scale. • Experience using Terraform for infrastructure provisioning. • Familiarity with Next.js / React (particularly for platform or internal tooling contexts).
Responsibilities
• Define, own, and evolve the high-level technical architecture for internal and client platforms and shared services, ensuring systems are scalable, secure, observable, and production-ready. • Organizing, distributing and translating backlog requirements from Product, UX and other disciplines, into detailed spec driven requirements for Agents to implement. • Synthesize user stories, site maps, content strategy, components and design systems, brand strategy, etc, into specifications that coding Agents will implement. • Designing and implementing digital solutions that have proper SEO/GEO, accessibility, performance and content management integrations. • Integrations with key Composable platforms such as Contentful, Contentstack, Algolia, Cloudinary, etc. • Champion spec-driven development as a core practice to improve clarity, quality, and predictability across teams. • Design and guide layered and distributed architectures, ensuring sound use of queues, caching, APIs, and database schemas while using teams of coding agents. • Establish standards for creating and consuming RESTful APIs across services. • Coach engineers on effective agent design, prompt architecture, and decision modeling. • Translate business goals into agent capabilities and system-level behaviours. • Define when autonomy is appropriate vs. when human oversight is required. • Lead the design and implementation of AI-powered systems, including: LLM integrations (Gemini 3, Claude Opus, GPT-4.x/5.x); Vector stores and Retrieval-Augmented Generation (RAG) pipelines. • Set direction for agent observability, debugging, and reliability in production environments. • Embed AI coding agents (Copilot, Claude Code, etc.) into development workflows to improve velocity, quality, and developer experience. • Continuously evaluate and introduce new AI tools and techniques to accelerate delivery while maintaining enterprise standards. • Promote disciplined experimentation and learning around emerging AI capabilities. • Provide technical leadership for systems built on Google Cloud Platform (GCP). • Oversee infrastructure design and provisioning, with Terraform as a preferred approach. • Ensure systems meet expectations for reliability, scalability, and operational excellence. • Lead and mentor senior engineers and technical leads across multiple teams. • Foster a calm, supportive, and solution-oriented engineering culture. • Partner with Engineering Managers, Product, and Delivery leaders to align technical initiatives with organizational goals. • Manage dependencies and technical risks across concurrent initiatives. • Identify and mitigate technical, architectural, and delivery risks early. • Drive continuous improvement in how software is designed, built, tested, and operated, especially through AI-enabled practices. • Ensure systems and teams are prepared for long-term maintainability and evolution.