Sr. GEN AI Developer with C#.NET
Location - NYC, NY
100 % Remote.
Candidate needs to work as per EST Timings.
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 validation, least privilege, dependency hygiene, secret management — without needing to be reminded.
Operational awareness. You build with logging, observability, and operability in mind. You have shipped things to production and kept them running.
AI-Augmented Engineering
This is a genuine differentiator for this role. We are not looking for someone who uses an AI chat assistant occasionally. We are looking for someone who has meaningfully changed how they work because of AI, and who can explain exactly how and why.
What this looks like in practice:
You use AI tooling actively across your development workflow — code generation, review, documentation, debugging, exploration, test scaffolding — and you have a clear, opinionated view of where it adds value and where it does not.
You understand enough about how large language models work — context windows, prompting patterns, retrieval-augmented generation, agent architectures — to be a capable practitioner without needing to be a researcher.
You are proficient with current AI development tooling: IDE-integrated assistants, agentic workflows, prompt engineering, and the emerging ecosystem of AI-native developer tools.
You can communicate your AI workflow to others clearly — what you use, when you use it, what you still do yourself, and what safety or quality checks you apply to AI-assisted output.
You recognize that AI amplifies strong engineers and cannot substitute for weak foundations. You bring both.
Experience
Significant professional experience in software engineering with demonstrable delivery across multiple domains, platforms, and team contexts.
A track record of technical leadership — you have influenced architecture decisions, shaped delivery practices, and helped teams level up.
Experience in cloud-native delivery environments, with real ownership of infrastructure and pipelines, not just application code.
Evidence of continuous learning: certifications, open source contributions, internal engineering initiatives, or simply a clear pattern of expanding your capability over time.
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