Own endtoend solution architecture for AI/innovation initiatives from problem framing and feasibility assessment through design, build, deployment, and production monitoring
Translate ambiguous business problems into concrete AIfirst solution designs, including buildvsbuyvsaugment recommendations and technology selection
Architect scalable, secure, cloudnative solutions spanning data pipelines, LLM/agent orchestration, integration layers, and application/UI tiers (Azure/AWS/Google Cloud Platform)
Define reference architectures and reusable design patterns for generative and agentic AI systems RAG pipelines, multiagent orchestration, tooluse/MCP integration, model routing and fallback strategies
Lead technical solutioning for proposals, RFPs, and client presales designing architecture diagrams, POC designs, cost/effort estimates, and technical narratives that support business cases
Evaluate and standardize the AI tooling landscape (LLM providers, AI assistants Copilot, Claude Code, Cursor, Gemini CLI, Amazon Q orchestration frameworks) and set adoption guidelines across delivery teams
Establish architecture governance: security, data privacy, model risk, cost controls, and compliance guardrails for AI systems in production
Author and maintain Architecture Decision Records (ADRs), nonfunctional requirement specs, and technical risk registers across the portfolio
Partner with delivery leadership, client stakeholders, and engineering teams to keep architecture aligned to commercial and business outcomes
Represent technical architecture in client workshops, steering committees, and executive/ELTfacing reviews
Mentor engineers and tech leads on architecture best practices, standards, and effective use of AIassisted development tools
Own the path from rapid prototype to scaled, observable, productiongrade system including deployment strategy and postlaunch monitoring design
8+ years of software engineering experience, including significant architecture, tech lead, or solution design capacity on production, enterprisescale systems
Demonstrated ownership of endtoend solution design across frontend, backend, and infrastructure not just componentlevel delivery
Deep, handson expertise with LLM/AI system architecture: RAG, agent orchestration, prompt engineering, model evaluation and selection, MCP or equivalent toolintegration patterns
Strong working command of AI code assistants and agent tools (Copilot, Amazon Q, Claude Code, GitHub Copilot, Gemini CLI, Cursor, or similar), with the ability to set standards for how teams use them not just personal usage
Proven experience contributing to or leading technical solutioning for proposals/RFPs architecture artifacts, feasibility studies, cost models, and businesscase framing
Strong stakeholder communication skills able to balance technical tradeoff discussions and executive/clientlevel narrative
Experience establishing or operating within architecture governance frameworks (security, compliance, cost, model risk)
Track record of mentoring engineering and driving adoption of AI practices across a delivery team
Familiarity with legacy modernization patterns (e.g., monolithtomicroservices, onpremtocloud migrations) is a plus
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