Description :

- Use GitHub Copilot as a primary development accelerator, demonstrating advanced prompt engineering, contextual steering, and validation techniques.

- Design and operate Model Context Protocol (MCP) servers to enable structured AI-assisted development workflows.

Build AI-augmented pipelines for :

1. Code generation and refactoring

2. Test case creation

3. Automated documentation

4. Static analysis and vulnerability detection

- Engineer high-quality prompts and reusable prompt libraries for consistent AI outputs.

- Integrate AI tooling into CI/CD workflows.

- Establish guardrails for AI-generated code quality, security, and compliance.

- Mentor team members in AI-Native engineering practices and Copilot optimization strategies.

- Continuously evaluate emerging AI development frameworks and tools.

Skills and Experience We Value :

Core Technical Skills :

- Bachelors or Masters degree in computer science or related field

- 10+ years of strong object-oriented programming experience using C#, .NET, .NET Core

- Solid experience building and deploying services on Microsoft Azure or other cloud platforms

- Experience with multi-threaded programming, event-driven architecture, messaging, and microservices

- Hands-on experience applying design patterns, writing unit tests, and performance optimization

- Proficiency with SQL and No SQL and modern RDBMs

- Experience with React, Node.js, GraphQL or similar

- Experience designing and implementing APIs

- Experience with Docker, Kubernetes, containerization, and CI/CD automation

- Knowledge of DevOps design patterns and best practices

- Desirable : Kafka, Confluent Cloud, NoSQL DB, Elastic Search, Specflow, Git, gRPC, protobuf, NuGet, MongoDB

AI-Native Development Expertise (Required) :

Candidates must demonstrate proven, hands-on expertise in :

- Advanced use of GitHub Copilot in enterprise software development

- Designing and deploying MCP servers

- Prompt engineering for production software workflows

- AI-assisted test generation and code review automation

- AI-augmented debugging and performance tuning

- Secure and compliant usage of AI-generated code

- Integrating AI tooling within DevOps pipelines

- Measuring and improving AI-driven developer productivity