Roles & Responsibilities
Key Responsibilities:
- Architectural Leadership: Design and implement end-to-end cloud-native architectures using .NET 8+ and modern distributed system patterns (Microservices, Event-Driven Architecture, Serverless, Modular Monolith).
- AI Integration: Lead the integration of Generative AI, LLMs, and RAG (Retrieval-Augmented Generation) patterns into enterprise workflows to enhance automation and user experience.
- AI-Augmented Engineering Leadership: Establish and evangelize "AI-First" development workflows. Guide the engineering team on the effective use of AI coding assistants (GitHub Copilot, Claude Code, Gemini) to accelerate delivery without compromising architectural integrity.
- Cross-Functional Collaboration: Act as a technical bridge between engineering squads and business stakeholders (Product, Sales, and Leadership) to ensure technical solutions align with commercial goals.
- Technical Governance & Mentorship: Establish coding standards and architectural blueprints. Actively mentor senior and staff engineers, fostering a team-oriented environment focused on growth and excellence.
- Cloud Strategy: Define and enforce best practices for cloud infrastructure, including multi-region deployments, disaster recovery, and cost optimization.
- Prototyping: Build Proof of Concepts (PoCs) for AI-driven features, evaluating the feasibility of various AI models and services (e.g., Azure OpenAI, Semantic Kernel).
Key Skills Required:
Core Cloud & .NET - 10+ Years Experience
- Expert-Level .NET: Mastery of C#, .NET Core/.NET 5-8, Web API, and Entity Framework.
- Advanced Cloud Architecture: Deep experience with major cloud providers (Azure, AWS, or GCP), specifically with App Services, Kubernetes (AKS/EKS), Service Bus, and Cloud Storage.
- DevOps & IaC: Proficiency in Infrastructure as Code (Terraform, Bicep, or ARM templates) and CI/CD pipeline automation (GitHub Actions, Azure DevOps).
- System Design: Proven track record in designing high-availability, low-latency distributed systems and complex data modeling (SQL and NoSQL).
AI & Modern Tech - 1-2 Years Experience
- AI-Augmented Engineering (AIAE): Deep proficiency in using AI pair programmers and agents (GitHub Copilot, Claude Code, Gemini Code Assist, or Cursor) to accelerate refactoring, unit testing, and documentation.
- AI Orchestration: Experience with frameworks like Semantic Kernel or LangChain for orchestrating AI workflows.
- LLM Integration: Hands-on experience working with OpenAI APIs, Azure OpenAI Service, or open-source models (Llama, Mistral).
- Vector Databases: Familiarity with vector search engines like Pinecone, Milvus, or Azure AI Search for implementing RAG patterns.
Human & Leadership Skills:
- Strategic Communication: Ability to translate highly technical AI and cloud concepts into clear, actionable insights for non-technical stakeholders and executives.
- Collaborative Mindset: A true team player who values collective success and thrives in a high-trust, peer-review-driven environment.
- Conflict Resolution & Influence: Skilled at navigating technical disagreements and building consensus across multiple engineering squads.
- Mentorship & Empathy: A commitment to coaching others and building a safe environment where team members feel comfortable experimenting with new AI technologies.
Added Advantages:
- Certifications: Microsoft Certified: Azure Solutions Architect Expert or Azure AI Engineer Associate.
- Community Presence: Experience speaking at conferences, writing technical blogs, or contributing to open-source projects.
- Prompt Engineering for Dev: Advanced skills in crafting system prompts for both application LLMs and developer productivity tools.
- Security Focus: Deep knowledge of Zero Trust architecture and AI security (prompt injection defense, data privacy in LLMs).
