Enterprise Solution Architect
Diligente Technologies, San Francisco, CA, United States
Employment Type: Contract to hire
Location: San Francisco Area as in office and/or Hybrid will be considered.
The intention will be contract to hire.
Apply (by clicking the relevant button) after checking through all the related job information below.
Full stack Enterprise Solution Architect We are seeking an experienced
Full Stack Enterprise Solution Architect
to design and guide the development of mission-critical technology solutions supporting investment management, research, trading, risk, and operational workflows. This role plays a key part in modernizing and evolving enterprise platforms while maintaining the stability, security, and governance required in a regulated financial environment. This role bridges architecture strategy and technical execution, working closely with stakeholders, engineering teams, and leadership to translate complex requirements into robust, future-proof architectures. The ideal candidate combines deep hands-on technical expertise with strong architectural judgment, communication skills, and a pragmatic approach to enterprise systems, along with an appreciation for the unique constraints of asset management systems—data accuracy, auditability, resilience, and long-term maintainability. Key Responsibilities Lead the end-to-end architecture of enterprise applications, spanning front-end, back-end, integration, data, and cloud infrastructure Partner with business (portfolio managers, analysts, operations, compliance), and technology stakeholders to understand requirements and translate them into scalable technical platform solutions Define and enforce architecture standards, patterns, and reference architectures Design APIs, microservices, event-driven systems, and integration layers to support enterprise workflows Evaluate and recommend technologies, platforms, and vendors aligned with long-term strategy Provide hands-on guidance and technical leadership to development teams Ensure solutions meet regulatory, security, performance, reliability, data governance and compliance requirements Review solution designs, code, and deployments to ensure architectural integrity Support cloud and hybrid architectures, including scalability, resilience, and cost optimization Mentor other solution architects / developers and help raise overall engineering and architectural maturity Required Qualifications 8+ years of experience in software development and solution architecture Strong full-stack expertise (modern JavaScript frameworks, backend services, APIs, and databases) Proven experience designing
enterprise-scale, high-reliability systems Deep understanding of cloud platforms (AWS, Azure, or GCP) and distributed architectures Experience with microservices, REST/GraphQL APIs, messaging, and integration patterns Solid grounding in security principles, authentication/authorization, and data protection Strong communication skills with the ability to explain complex technical concepts to non-technical audiences Preferred Qualifications Experience in asset management, investment management, banking, or financial services, or large enterprise environments Familiarity with DevOps, CI/CD pipelines, and infrastructure-as-code Experience with containerization and orchestration (Docker, Kubernetes) Exposure to data platforms, analytics, or event streaming technologies Experience with enterprise AI platforms (ChatGPT Enterprise, Copilot Studio, Gemini, Snowflake Cortex AI) and AI application use cases Background in regulated industries for compliance awareness. Prior experience mentoring engineers or leading architectural reviews Technical Skill breakdown Backend Development .NET Core / .NET 6+: Expertise in building scalable APIs and microservices. C#: Strong command of asynchronous programming, LINQ, and design patterns. API Design: RESTful and GraphQL services for integration. Frontend Development JavaScript/TypeScript: Advanced proficiency. Frameworks: React.js, Angular, or Vue.js for responsive UI design. UI/UX: Accessibility standards, component-based architecture, and state management. Database & Data Engineering Snowflake: Deep understanding of data warehousing, SQL optimization, and integration with AI workflows. Familiarity with dbt for transformations and Azure SQL as fallback AI & Emerging Tech Integration Generative AI & LLMs: Experience with OpenAI, Claude, Gemini, and Azure OpenAI for building intelligent features. Prompt Engineering: Crafting effective prompts for AI-driven workflows. Agentic Architecture: Implementing Retrieval-Augmented Generation (RAG), embeddings, and vector search for AI agents. Model Orchestration: Deploying and integrating models from Hugging Face, Vertex AI, or OpenAI APIs Cloud & DevOps Cloud Platforms: Azure expertise (App Services, Functions, Identity & Security). CI/CD Pipelines: GitHub Actions, Jenkins for automated deployments. xywuqvp Containerization: Docker and Kubernetes for scalable deployments. Security: OAuth, DLP, encryption (Double Key Encryption), and compliance with InfoSec standard Model & AI Governance Understanding of data protection policies, secure credential management, and governance frameworks for AI integration
Apply (by clicking the relevant button) after checking through all the related job information below.
Full stack Enterprise Solution Architect We are seeking an experienced
Full Stack Enterprise Solution Architect
to design and guide the development of mission-critical technology solutions supporting investment management, research, trading, risk, and operational workflows. This role plays a key part in modernizing and evolving enterprise platforms while maintaining the stability, security, and governance required in a regulated financial environment. This role bridges architecture strategy and technical execution, working closely with stakeholders, engineering teams, and leadership to translate complex requirements into robust, future-proof architectures. The ideal candidate combines deep hands-on technical expertise with strong architectural judgment, communication skills, and a pragmatic approach to enterprise systems, along with an appreciation for the unique constraints of asset management systems—data accuracy, auditability, resilience, and long-term maintainability. Key Responsibilities Lead the end-to-end architecture of enterprise applications, spanning front-end, back-end, integration, data, and cloud infrastructure Partner with business (portfolio managers, analysts, operations, compliance), and technology stakeholders to understand requirements and translate them into scalable technical platform solutions Define and enforce architecture standards, patterns, and reference architectures Design APIs, microservices, event-driven systems, and integration layers to support enterprise workflows Evaluate and recommend technologies, platforms, and vendors aligned with long-term strategy Provide hands-on guidance and technical leadership to development teams Ensure solutions meet regulatory, security, performance, reliability, data governance and compliance requirements Review solution designs, code, and deployments to ensure architectural integrity Support cloud and hybrid architectures, including scalability, resilience, and cost optimization Mentor other solution architects / developers and help raise overall engineering and architectural maturity Required Qualifications 8+ years of experience in software development and solution architecture Strong full-stack expertise (modern JavaScript frameworks, backend services, APIs, and databases) Proven experience designing
enterprise-scale, high-reliability systems Deep understanding of cloud platforms (AWS, Azure, or GCP) and distributed architectures Experience with microservices, REST/GraphQL APIs, messaging, and integration patterns Solid grounding in security principles, authentication/authorization, and data protection Strong communication skills with the ability to explain complex technical concepts to non-technical audiences Preferred Qualifications Experience in asset management, investment management, banking, or financial services, or large enterprise environments Familiarity with DevOps, CI/CD pipelines, and infrastructure-as-code Experience with containerization and orchestration (Docker, Kubernetes) Exposure to data platforms, analytics, or event streaming technologies Experience with enterprise AI platforms (ChatGPT Enterprise, Copilot Studio, Gemini, Snowflake Cortex AI) and AI application use cases Background in regulated industries for compliance awareness. Prior experience mentoring engineers or leading architectural reviews Technical Skill breakdown Backend Development .NET Core / .NET 6+: Expertise in building scalable APIs and microservices. C#: Strong command of asynchronous programming, LINQ, and design patterns. API Design: RESTful and GraphQL services for integration. Frontend Development JavaScript/TypeScript: Advanced proficiency. Frameworks: React.js, Angular, or Vue.js for responsive UI design. UI/UX: Accessibility standards, component-based architecture, and state management. Database & Data Engineering Snowflake: Deep understanding of data warehousing, SQL optimization, and integration with AI workflows. Familiarity with dbt for transformations and Azure SQL as fallback AI & Emerging Tech Integration Generative AI & LLMs: Experience with OpenAI, Claude, Gemini, and Azure OpenAI for building intelligent features. Prompt Engineering: Crafting effective prompts for AI-driven workflows. Agentic Architecture: Implementing Retrieval-Augmented Generation (RAG), embeddings, and vector search for AI agents. Model Orchestration: Deploying and integrating models from Hugging Face, Vertex AI, or OpenAI APIs Cloud & DevOps Cloud Platforms: Azure expertise (App Services, Functions, Identity & Security). CI/CD Pipelines: GitHub Actions, Jenkins for automated deployments. xywuqvp Containerization: Docker and Kubernetes for scalable deployments. Security: OAuth, DLP, encryption (Double Key Encryption), and compliance with InfoSec standard Model & AI Governance Understanding of data protection policies, secure credential management, and governance frameworks for AI integration