Application Architect, IT Architects

Application Architect Enterprise AI Program

Position Summary

As an Application Architect in the Enterprise AI Program you will provide technical direction architectural oversight and hands-on engineering leadership across scrum teams building production AI-enabled software.

This role sits between enterprise/program architecture and delivery teams helping translate business needs and platform strategy into practical scalable secure and maintainable technical solutions.

This is a senior technical leadership role for someone who can:

• Shape architecture and implementation patterns
• Guide teams building AI-enabled applications agents workflows and integrations
• Validate technical direction through code demos and proofs of concept
• Establish reusable patterns templates and reference implementations
• Stay close to the codebase through code reviews technical spikes troubleshooting and situational code contributions
• Evaluate emerging AI technologies and educate the team through practical examples

This role is not a full-time feature development position but it is also not a hands-off architecture role. The Application Architect is expected to remain technically close to the platform and contribute directly when architectural complexity delivery risk production issues or emerging platform patterns require senior technical involvement.

You will help lead architecture across:

• Enterprise AI platform capabilities
• LLM-powered agents and agent workflows
• Retrieval-augmented generation or RAG patterns
• Model Context Protocol-style tool integrations
• Durable workflow and orchestration patterns
• Internal and external AI-enabled applications
• Secure integrations with Salesforce and internal Ascensus systems
• Observability evaluation testing and production support patterns
• AI-assisted engineering practices using tools such as Cursor Claude Code or similar platforms

We are looking for an architect who is practical hands-on delivery-oriented and deeply curious about where AI engineering is headed. You should be able to set direction without creating unnecessary complexity mentor engineers without becoming a bottleneck and turn emerging technology into working examples that help teams move faster with confidence.

What Were Looking For

We are looking for a technical leader who can:

• Translate business needs into practical AI platform architecture.
• Provide technical direction across multiple scrum teams.
• Align implementation decisions with enterprise architecture and platform strategy.
• Guide teams building agents RAG pipelines workflows tools integrations and AI-enabled applications.
• Establish reusable patterns templates reference implementations and engineering standards.
• Review designs and coach teams toward secure scalable observable and maintainable solutions.
• Stay actively connected to the codebase through code reviews technical spikes POCs reference implementations troubleshooting and situational code contributions.
• Evaluate emerging AI tools models frameworks orchestration patterns and development practices.
• Translate technical exploration into practical guidance demos reusable examples and implementation standards.
• Help teams resolve technical impediments and production issues.
• Promote consistency and reuse across teams without slowing delivery.
• Communicate clearly with technical and non-technical stakeholders.

Required Qualifications

• 8 years of professional software engineering experience.
• 2 years in a technical lead application architect solution architect staff engineer or comparable technical leadership role.
• Bachelors degree in Computer Science Computer Information Systems Business Information Systems a related technical field or equivalent practical experience.
• Strong experience designing building and supporting production software in medium to large business environments.
• Demonstrated ability to remain hands-on with the codebase through code reviews proof-of-concept development technical spikes complex troubleshooting and direct code contributions.
• Experience building proofs of concept reference implementations technical demos templates or reusable engineering patterns.
• Strong experience with modern software engineering practices including:
  • Clean code
  • Source control
  • CI/CD
  • Automated testing
  • Design patterns
  • Refactoring
  • API design
  • Observability
  • Production support
• Strong experience with one or more modern programming languages and platforms such as:
  • Python
  • JavaScript / TypeScript
  • SQL
  • Similar modern development platforms
• Experience designing distributed systems service integrations APIs workflow logic or platform capabilities.
• Experience with LLM-powered systems or AI-enabled applications such as:
  • RAG
  • Chatbots
  • Agent workflows
  • Prompt engineering
  • Tool use
  • AI-assisted application development
• Strong understanding of architecture principles including:
  • Modularity
  • Reusability
  • Scalability
  • Reliability
  • Security
  • Maintainability
  • Observability
  • Cost awareness
• Experience guiding teams through technical design estimation implementation and production readiness.
• Strong troubleshooting and root-cause analysis skills across application code integrations logs traces telemetry and production behavior.
• Experience mentoring coaching and influencing engineers without requiring direct reporting authority.
• Excellent communication skills with the ability to explain technical tradeoffs to engineers product partners business stakeholders and senior leaders.
• Comfort operating in ambiguous fast-moving environments where AI capabilities tools and platform patterns continue to evolve.

Preferred Qualifications

Experience with one or more of the following is helpful but not required:

• Enterprise AI platform architecture
• Azure AI Foundry Azure AI services or similar cloud AI platforms
• Azure DevOps Azure App Service Azure API Management or related Microsoft cloud tools
• LLM application architecture using models from OpenAI Anthropic Microsoft or similar providers
• Agentic applications and multi-agent workflow patterns
• RAG architecture retrieval quality chunking strategies embeddings vector databases and reranking
• Model Context Protocol function calling tool calling or enterprise tool integration patterns
• MCP registry tool governance tool security or act-as-user integration patterns
• Durable workflow platforms such as Temporal
• Event-driven systems message queues or orchestration patterns
• Salesforce integrations or enterprise system integrations
• Observability platforms such as Langfuse New Relic OpenTelemetry Azure Log Analytics or similar tools
• AI evaluation frameworks golden tests prompt/version management or quality measurement practices
• Secure software design for systems that handle sensitive or regulated data
• DevOps infrastructure deployment automation containerization or cloud-native application patterns
• AI-powered development tools such as Cursor Claude Code or similar tools
• Creating technical demos POCs or internal enablement materials to help engineering teams adopt new technologies
• Technical documentation architecture decision records solution diagrams and executive-level technical communication
• Working with SDETs DevOps engineers support engineers software managers product owners and enterprise architects

Key Areas of Ownership

Architecture Direction

• Set technical direction for AI platform capabilities and implementation patterns.
• Partner with other architects to align team-level designs with broader platform strategy.
• Ensure solutions are secure scalable reliable observable and maintainable.
• Help teams make good tradeoff decisions around speed quality complexity cost and long-term supportability.

AI Platform Patterns

• Guide architecture for LLM-powered agents RAG pipelines prompts skills tools and workflows.
• Define reusable patterns for AI application development.
• Establish standards for tool integrations orchestration observability evaluation and production readiness.
• Support responsible AI engineering practices that improve accuracy transparency reliability and user trust.

Hands-On Technical Leadership

• Stay close to the codebase through regular code reviews design reviews POCs reference implementations and complex troubleshooting.
• Contribute directly to application platform workflow integration or agent code when senior technical involvement is needed.
• Build working demos technical spikes starter templates and reference implementations.
• Experiment with emerging AI technologies tools models frameworks and orchestration patterns.
• Turn technical exploration into practical team guidance and reusable implementation standards.
• Assist with production issues requiring senior engineering judgment.
• Use hands-on learning and code-level involvement to coach engineers and raise the technical bar.

Collaboration and Communication

• Work closely with program architects application architects software managers engineers SDETs DevOps engineers support teams product partners and business stakeholders.
• Explain architecture decisions and tradeoffs clearly.
• Document solution designs patterns diagrams standards and decision records.
• Promote reuse and consistency across teams.
• Help socialize new technologies patterns and platform capabilities.

What Success Looks Like

You will be successful in this role if you:

• Set clear technical direction that teams can actually execute.
• Help teams move faster without creating unnecessary complexity or long-term risk.
• Build reusable patterns that improve consistency quality and delivery speed.
• Make AI platform capabilities easier for teams to understand use test and support.
• Remain close enough to the codebase to make architecture decisions that are practical credible and executable.
• Contribute directly when the team needs senior technical help.
• Turn emerging AI technologies into practical examples demos and reusable patterns.
• Mentor engineers and raise the technical bar across teams.
• Communicate architecture in a way that is useful to engineers leaders and business partners.
• Help the Enterprise AI Program scale from individual solutions to a repeatable platform model.

Supervision

This position does not have direct reports.

However this role provides technical leadership mentoring coaching and architectural oversight across multiple scrum teams within the Enterprise AI Program.

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