Cloud Solutions Architect / Database Engineer / AI Platform Architect (AZ , Phoenix )

Responsibilities

• Architect and implement scalable, secure, and highly available cloud environments for enterprise applications.
• Design the overall backend architecture supporting web applications, internal systems, integrations, and AI-powered solutions.
• Design, build, and maintain production database environments, including schemas, tables, relationships, stored procedures, views, indexing strategies, and data-access patterns.
• Develop and optimize SQL Server databases for performance, scalability, reliability, data integrity, and security.
• Establish database standards covering data modeling, normalization, indexing, query optimization, auditing, backup, recovery, and disaster recovery.
• Design and develop secure?RESTful APIs and backend services using C#, ASP.NET Core, and related .NET technologies.
• Build well-structured API and service layers that allow front-end developers to consume data and business functionality without requiring direct access to backend systems or databases.
• Define API contracts, request/response models, validation standards, error handling, versioning, documentation, and integration patterns.
• Implement authentication and authorization solutions using technologies and standards such as?OAuth 2.0, OpenID Connect, JWT, SSO, RBAC, and enterprise identity providers.
• Design and enforce application and API security standards, including SSL/TLS, encryption, secrets management, certificate management, secure configuration, and least-privilege access.
• Implement secure communication between cloud services, databases, APIs, external systems, AI services, and front-end applications.
• Design cloud networking and infrastructure components including application hosting, databases, storage, identity, networking, firewalls, gateways, load balancing, monitoring, logging, and availability strategies.
• Develop integration architectures for internal systems, third-party applications, vendor APIs, and enterprise platforms.
• Design data pipelines, ETL processes, data transformation services, and system-to-system integrations where required.
• Establish logging, monitoring, auditing, alerting, and observability standards across backend services and cloud infrastructure.
• Design scalable architectures capable of supporting increasing users, transaction volumes, data volumes, integrations, AI workloads, and application workloads.
• Implement caching, asynchronous processing, queues, background services, and other distributed architecture patterns when appropriate.
• Develop and maintain CI/CD pipelines and infrastructure deployment processes.
• Work closely with front-end developers to define API requirements, data contracts, authentication flows, AI service interactions, and integration standards.
• Design and implement AI workflow architectures that enable Large Language Models to perform complex business functions by defining process sequences, system instructions, prompt strategies, retrieval mechanisms, examples, evaluation methods, tool interactions, and orchestration workflows.
• Architect AI systems capable of incorporating enterprise knowledge and business processes through structured process definitions, contextual examples, retrieval, tool use, and iterative refinement so that AI can reliably execute operational business tasks.
• Design Retrieval-Augmented Generation (RAG) architectures that securely retrieve relevant enterprise information from databases, documents, APIs, vector stores, and other approved business data sources.
• Design secure AI integration patterns that control how LLMs access enterprise databases, APIs, internal systems, and sensitive business information.
• Establish AI evaluation, testing, monitoring, and quality standards to measure accuracy, reliability, consistency, security, and effectiveness of AI-powered workflows.
• Collaborate with business stakeholders and development teams to translate application requirements and business processes into technical and AI architectures.
• Evaluate technical risks, scalability requirements, security concerns, infrastructure costs, AI usage costs, and architectural tradeoffs.
• Conduct architecture and code reviews and establish backend, database, API, cloud, security, and AI development standards.
• Provide technical leadership and mentoring to developers working within the architecture.