Senior .NET Solution Architect

Responsibilities: Define end-to-end solution architecture, including system decomposition, integration patterns, data flows, and deployment topology. Lead the design of microservices and clean architecture implementations that are testable, maintainable, and aligned with business goals. Set technical standards, coding guidelines, and architecture decision records (ADRs) for engineering teams. Review designs and code for performance, security, scalability, and compatibility. Partner with product, engineering, and infrastructure teams to translate requirements into technical roadmaps. Evaluate and recommend technologies, frameworks, and tooling, weighing trade-offs in cost, risk, and time to market. Mentor senior and mid-level engineers and build a culture of engineering excellence. Drive adoption of AI-assisted development tools and practices across the team, with appropriate governance.

Required Skills: Backend and .NET: Strong expertise in .NET, C#, and ASP.NET Core, with a solid grasp of SOLID principles and clean architecture, along with strong experience in Angular. Distributed systems: Designing and implementing microservices, including service boundaries, inter-service communication, and resilience patterns. APIs and security: RESTful and GraphQL API design, versioning and backward compatibility, and application security (OAuth2/OpenID Connect, JWT, OWASP Top 10, secrets management).Data: SQL Server and relational database design, query optimization, and data access using Dapper, EF Core, and GraphQL where appropriate. Frontend: Working knowledge of Angular for building or reviewing front-end architecture and integrations. Observability and DevOps: Logging, metrics, and distributed tracing (e.g., OpenTelemetry, Application Insights, Grafana, or similar), along with CI/CD pipeline design (Azure DevOps, GitHub Actions, or Jenkins) and containerization. AI-assisted development: Practical experience using AI coding assistants and integrating AI-assisted workflows into the SDLC, including prompt design, code review of generated output, and quality safeguards. System design and problem solving: Ability to design for scale, availability, and cost, and to break down ambiguous problems into clear technical plans.

Preferred Skills: Cloud platforms: Hands-on experience with Azure or AWS.Containers and orchestration: Docker and Kubernetes, with an understanding of Helm and GitOps practices. Cloud-native patterns: Event-driven architecture, CQRS, event sourcing, and message brokers (Kafka, RabbitMQ, or Azure Service Bus).AI and LLM integration: Experience integrating LLM-based features into enterprise applications (e.g., Azure/AWS OpenAI, retrieval-augmented generation, vector databases), along with awareness of AI governance and data privacy. Security and compliance: Zero-trust principles, identity platforms (Azure AD / Entra ID), and familiarity with standards such as SOC 2, ISO 27001, or GDPR.Modern frontend practices: Component-driven design, micro-frontends, and performance optimization for SPAs.