AI Healthcare Solution Architect - Remote - Contract to Hire

AI Solution Architect

C2H

Remote

Healthcare Domain

Role & Responsibilities

• Own end-to-end 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 AI-first solution designs, including build-vs-buy-vs-augment recommendations and technology selection
• Architect scalable, secure, cloud-native 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, multi-agent orchestration, tool-use/MCP integration, model routing and fallback strategies
• Lead technical solutioning for proposals, RFPs, and client pre-sales — producing architecture diagrams, POC designs, cost/effort estimates, and technical narratives that support business cases
• Evaluate and standardize the AI tooling landscape (LLM providers, AI code 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), non-functional 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/ELT-facing reviews
• Mentor engineers and tech leads on architecture best practices, code quality standards, and effective use of AI-assisted development tools
• Own the path from rapid prototype to scaled, observable, production-grade system — including deployment strategy and post-launch monitoring design

Required Qualifications

• 8+ years of software engineering experience, including significant time in an architecture, tech lead, or solution design capacity on production, enterprise-scale systems
• Demonstrated ownership of end-to-end solution design across frontend, backend, data, and cloud infrastructure — not just component-level delivery
• Deep, hands-on expertise with LLM/AI system architecture: RAG, agentic/multi-agent design, prompt and context engineering, model evaluation and selection, MCP or equivalent tool-integration patterns
• Strong working command of AI code assistants and agentic dev tools (Claude Code, GitHub Copilot, Amazon Q, 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 client proposals/RFPs — architecture artifacts, feasibility studies, cost models, and business-case framing
• Strong stakeholder communication skills — able to move fluidly between deep technical trade-off discussions and executive/client-level narrative
• Experience establishing or operating within architecture governance frameworks (security, compliance, cost, model risk)
• Track record of mentoring engineers and driving adoption of new practices or tooling across a delivery team
• Familiarity with legacy modernization patterns (e.g., monolith-to-microservices, on-prem-to-cloud migrations) is a plus