Technical Solution Architect/Engineering Manager

Role Overview

We are looking for a Technical Solution Architect to drive the design and implementation of AI-powered enterprise solutions, combining solution architecture expertise with hands-on software engineering and cloud development.

The role focuses on transforming business challenges and manual processes into scalable, secure, and reliable AI-driven applications and automation workflows.

Working in a dynamic R&D environment, the architect will design end-to-end solutions, develop AI agents and prototypes, integrate enterprise systems, and establish reusable engineering standards. The position requires close collaboration with business stakeholders and technical teams, as well as the ability to independently drive initiatives from initial discovery through production deployment.

Key Responsibilities

• Solution Architecture: Design end-to-end enterprise and AI-powered solutions, defining architecture, technology choices, integrations, APIs, authentication, data flows, and deployment approaches.
• Hands-on AI Development: Build prototypes, LLM-powered applications, AI agents, automations, and production-ready solutions using modern AI technologies.
• Agentic AI & Development Workflows: Design and improve AI-assisted software development workflows, create reusable agent skills, custom instructions, and automation frameworks.
• Azure Cloud Architecture: Design, deploy, and optimize Azure infrastructure for AI applications, ensuring scalability, security, reliability, and cost efficiency.
• Rapid Prototyping & Innovation: Translate ambiguous business requirements into working prototypes, validate assumptions, and iterate quickly based on stakeholder feedback.
• Enterprise Integrations: Connect AI solutions with enterprise platforms, internal APIs, databases, and business systems using REST APIs, MCP, webhooks, and workflow automation.
• Technical Leadership: Own technical decisions, identify architectural challenges, and drive solutions from concept through implementation and delivery.
• Architecture Governance & Engineering Standards: Establish reusable architecture patterns, security guardrails, engineering standards, and best practices across teams.
• Security & Compliance: Ensure secure solution design through appropriate access controls, identity management, secrets management, and enterprise security practices.
• Continuous Improvement: Research emerging AI technologies, evaluate new tools, and identify opportunities to accelerate engineering and business processes.
• Knowledge Sharing: Mentor engineers, document reusable solutions, and support teams in adopting AI-assisted development practices.
• End-to-End Ownership: Take responsibility for technical outcomes, troubleshoot blockers, and coordinate across teams to ensure successful delivery.

Must-Have Requirements

• Solution Architecture
• Strong experience designing end-to-end enterprise solutions, including integrations, APIs, authentication, data flows, and cloud infrastructure.
• Azure Cloud
• Practical experience with Azure Functions, App Services, Storage, Key Vault, Entra ID, networking, and identity management.
• Generative AI & Agentic Systems
• Practical experience building LLM-powered applications, AI agents, tool calling, RAG, and multi-step workflows.
• AI-Assisted Development
• Strong experience with tools such as Cursor, GitHub Copilot, or Claude Code.
• Experience with agent instructions, skills, and automated development workflows.
• Software Engineering
• Strong development background in at least one programming language: Python, C#, or TypeScript.
• Experience building production-ready applications.
• Integration & Automation
• Experience connecting enterprise systems using REST APIs, MCP, webhooks, and workflow automation.
• Rapid Prototyping
• Ability to turn ambiguous business requirements into working prototypes quickly.
• Experience validating assumptions and iterating based on feedback.
• Security & Governance
• Understanding of enterprise security, RBAC, managed identities, secrets management, and secure AI solution design.
• Technical Ownership
• Ability to independently drive technical initiatives from discovery through implementation and deployment.
• Communication
• Strong English communication skills.
• Comfortable working directly with business stakeholders and explaining technical decisions.

Nice-to-Have Requirements

• Microsoft AI Ecosystem: Experience with Azure AI Foundry, Azure AI Search, Azure OpenAI, Microsoft Copilot Studio, and Microsoft Graph API.
• Agentic Frameworks: Familiarity with Semantic Kernel, LangGraph, AutoGen, or similar agent orchestration frameworks.
• AI Development Workflows: Experience designing reusable AI agent skills, custom instructions, MCP servers, and agentic SDLC workflows.
• Infrastructure as Code: Experience with Terraform, Bicep, ARM templates, and automated cloud provisioning.
• DevOps & CI/CD: Experience with GitLab CI/CD, GitHub Actions, Docker, and automated deployment pipelines.
• Enterprise Integrations: Experience integrating SharePoint, Salesforce, Jira, Confluence, Microsoft Teams, and other enterprise platforms.
• AI Evaluation & Observability: Knowledge of LLM evaluation, prompt optimization, tracing, monitoring, and cost management.
• Enterprise Architecture: Experience working in regulated industries such as financial services, with security, compliance, and governance constraints.
• Technical Enablement: Experience mentoring engineers, creating reusable engineering standards, and enabling teams to adopt new technologies.
• Innovation & R&D: Experience exploring emerging AI technologies, running technical experiments, and turning successful prototypes into reusable solutions.

Technical Environment

• Architecture: Enterprise Solution Architecture, API Design, Cloud Architecture, Distributed Systems.
• Cloud: Microsoft Azure, Azure Functions, App Services, Storage, Key Vault, Entra ID.
• AI & Automation: Generative AI, LLMs, AI Agents, RAG, Tool Calling, Agentic Workflows, Intelligent Automation.
• AI Development Tools: Cursor, GitHub Copilot, Claude Code.
• AI Platforms: Azure AI Foundry, Azure AI Search, Azure OpenAI, Microsoft Copilot Studio.
• Programming Languages: Python, C#, TypeScript.
• Agentic Frameworks: Semantic Kernel, LangGraph, AutoGen.
• Integrations: REST APIs, MCP, Webhooks, Microsoft Graph API, Enterprise Platforms.
• Infrastructure & DevOps: Terraform, Bicep, ARM Templates, Docker, GitLab CI/CD, GitHub Actions.
• Security: RBAC, Managed Identities, Secrets Management, Enterprise Security.
• Engineering Practices: AI-Assisted Development, Agentic SDLC, Rapid Prototyping, Architecture Governance, Technical Standards.

Ideal Candidate Profile

The ideal candidate is a proactive, hands-on AI Solution Architect with a strong engineering mindset and a genuine passion for emerging AI technologies.

• Proactive & Initiative-Driven: Takes initiative, challenges existing approaches, and identifies opportunities for improvement.
• Comfortable with Ambiguity: Thrives in a fast-paced R&D environment with evolving requirements and uncertainty.
• Strong Builder Mentality: Prefers experimenting, prototyping, and validating ideas rather than spending excessive time on theoretical discussions.
• Passionate About AI: Actively explores emerging AI tools and technologies and applies them in real-world scenarios.
• Independent & Accountable: Makes technical decisions and takes ownership without requiring constant guidance.
• Scalability-Oriented: Focuses on creating scalable, reusable solutions that benefit multiple teams.
• Pragmatic Engineering Mindset: Balances speed of delivery with engineering quality, security, and maintainability.
• Collaborative: Enjoys working with both technical and non-technical stakeholders.

Collaboration

• Business stakeholders and clients.
• AI Platform teams.
• Software Engineering teams.
• IT and DevOps teams.
• Cross-functional technical teams.

Key Expectations

• Design and build solutions, not just provide architectural advice.
• Move quickly from business problems to working prototypes and production-ready applications.
• Take end-to-end technical ownership.
• Drive AI adoption and engineering innovation.
• Develop reusable AI solutions, agent skills, frameworks, and engineering standards.
• Integrate AI capabilities into existing enterprise environments.
• Collaborate directly with clients and business stakeholders.
• Operate independently in a dynamic R&D environment.