Sr. GEN AI Developer with C#.NET

Our Client which is a large Payroll Firm is urgently looking to hire a Sr. GEN AI Developer with C#.NET

Sr. GEN AI Developer with C#.NET

Location - NYC, NY

Number of Roles - 5.

100 % Remote.

Skills - GEN AI Development, RAG, LLM’s, C#.NET

About the Role

We are looking for a Senior Software Engineer who brings deep engineering instincts, broad technical capability, and a genuine curiosity for solving hard problems. This is not a role defined by a fixed technology stack — it is a role defined by the quality of your thinking, the depth of your foundation, and your ability to operate effectively across the full engineering landscape.

You will work on complex, high-stakes delivery where the tools, platforms, and challenges evolve continuously. The engineers who thrive here do not master a checklist of technologies — they build a strong enough foundation that picking up the next tool is a moderate lift, not a reinvention of themselves.

What You Will Do

Design and deliver software solutions across the full engineering lifecycle — from architecture and infrastructure through to application logic, integration, testing, and deployment.

Contribute to platform and tooling decisions, not just implementation.

Bring engineering rigour to every layer of the stack: infrastructure, backend services, APIs, front-end experiences, pipelines, and automation.

Write code that is production-ready from the start — tested, documented, observable, and maintainable.

Make deliberate use of AI tooling throughout your development workflow, and help raise the standard for how the wider team works with AI.

Mentor and influence peers through code review, pair programming, technical documentation, and architectural input.

Move fluidly between domains as delivery demands it, while going deep where depth is needed.

What You Bring

Engineering Foundation

This matters more than any individual tool. You think in patterns — not just design patterns in the GoF sense, but in the broader sense established by practitioners like Martin Fowler, Eric Evans, Sam Newman, and others who have codified how to build systems that last. When you encounter a new platform or framework, you are mapping it to concepts you already hold. The learning curve is moderate, not steep, because the foundations are solid.

Strong grasp of enterprise application architecture, distributed systems design, and integration patterns.

Experience applying domain-driven design, clean architecture, or equivalent structural approaches to real production systems.

Comfort reasoning about trade-offs — coupling, cohesion, consistency, scalability, operability — and articulating those trade-offs to stakeholders.

Technical Breadth and Depth

You have a working toolbox built up over years of real delivery. Some tools you know deeply; others you know well enough to be dangerous and to go deeper quickly. The specific tools matter less than the breadth of contexts you have worked in and the depth you have demonstrated in at least several areas.

Representative technologies used in this environment include:

Languages and frameworks: .NET / C#, Python, JavaScript / TypeScript, PowerShell, Angular

Cloud and infrastructure: Microsoft Azure, Terraform, Infrastructure as Code (IaC), cloud-native architecture patterns

DevOps and delivery: GitHub, GitHub Actions, CI/CD pipeline design and implementation

Cross-cutting: API design, observability, security practices, dependency management

This list is representative, not exhaustive. The environment changes. We expect you to change with it.

Proficiency in Any of the GEN AI Technologies -

Copilot or Google Gemini or Claude or OpenAI or Glean or Watson or Zapier or Jasper or Firefly or RunwayML or Grok or AgenticAI or Devin or Vertex

Software Engineering Practices

We expect engineering practices to be non-negotiable, not aspirational:

Developer-led testing. You write tests as part of development, not after. You understand the testing pyramid, know when to push toward unit coverage and when integration or contract tests are the right tool, and you advocate for testable design from the start.

Documentation as a first-class output. Code is written once and read many times. You document your reasoning, your interfaces, your runbooks, and your decisions. You understand the difference between documentation that helps and documentation that clutters.

Secure by default. You apply security thinking throughout the development lifecycle — input v…