AI Technical Architect

AI Technical Architect Location: Richardson, TX - Onsite - Candidate should go to the office 3 days a Week Look for Only USC / GC / GC - EAD for Full Time Full-time / Contract Role Summary: We are seeking an AI Architect with strong experience designing and deploying enterprise-scale AI and GenAI solutions. The role focuses on architecting agentic AI systems, integrating GenAI capabilities into corporate platforms, and ensuring models are evaluated, governed, tested, and deployed reliably at scale. This role requires architect-level expertise on Google Cloud Platform (GCP), deep understanding of Model Context Protocols, and hands-on experience delivering production-grade AI systems in regulated or enterprise environments. Good to have healthcare domain experience. Key Responsibilities AI Agentic System Architecture Architect agentic AI systems including planners, executors, validators, and supervisors. Design orchestration patterns for multi-agent workflows and autonomous decisioning. Define agent interaction models using Model Context Protocol (MCP) or equivalent context-sharing mechanisms. Implement human-in-the-loop and override mechanisms for enterprise governance. GenAI in Enterprise Environments Design and integrate GenAI solutions within corporate IT ecosystems, ensuring security, compliance, and scalability. Enable GenAI capabilities across internal applications, workflows, and platforms. Define prompt strategies, context windows, memory handling, and fallback logic suitable for enterprise usage. Ensure responsible use of GenAI aligned with corporate policies. Cloud Platform Architecture (GCP) Architect and deploy AI solutions on Google Cloud Platform (GCP). Leverage GCP services such as: Compute and container orchestration Managed AI services Messaging and event-driven architectures Secure API management Design scalable, resilient, and cost-efficient cloud-native AI architectures. Model Evaluation Testing Define model evaluation metrics for GenAI and ML systems, including accuracy, relevance, latency, and cost. Establish testing strategies for AI systems: Functional testing Regression testing Performance and load testing Bias, drift, and robustness testing Design validation frameworks to measure model effectiveness and reliability over time. Model Deployment Lifecycle Management Architect model deployment strategies for production environments (batch, real-time, hybrid). Design CI/CD pipelines for model versioning, deployment, rollback, and monitoring. Enable observability for AI systems, including logging, tracing, and performance metrics. Support continuous improvement through monitoring and retraining strategies. Design APIs and service contracts for AI-enabled capabilities. Integrate AI services with enterprise systems, data platforms, and messaging infrastructure. Ensure interoperability across frontend, backend, and data layers. Define guardrails for AI systems including explainability, traceability, and auditability. Ensure compliance with enterprise security, privacy, and regulatory requirements. Design fault-tolerant and resilient AI systems with graceful degradation. Leadership Collaboration Provide architectural leadership and design guidance to engineering teams. Partner with product, data science, DevOps, and security teams. Communicate complex AI architecture decisions clearly to technical and non-technical stakeholders. Required Technical Skills Strong experience designing agentic AI systems and autonomous workflows. Hands-on experience with Model Context Protocol (MCP) or equivalent context management frameworks. Deep understanding of GenAI systems in enterprise environments. Experience working with LLMs and GenAI models. Architect-level experience with Google Cloud Platform (GCP). Strong knowledge of model evaluation metrics and validation techniques. Understanding of prompt engineering, context handling, and inference optimization. Proficiency in Python and/or JavaScript/TypeScript. Experience with microservices architectures, REST/gRPC APIs. Knowledge of event-driven systems and messaging platforms. Experience with model deployment, versioning, and lifecycle management. Familiarity with CI/CD pipelines, containerization, and cloud-native deployments. Ability to design systems for scalability, resilience, and cost efficiency. Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related field. 15-20+ years of experience in software architecture or engineering, with strong AI system design experience for Healthcare customers. is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodation also are available in the hiring process for applicants with disabilities. Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at .

Created: 2026-03-10