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Lead AI .NET Developer; Remote; W2

Remote / Online - Candidates ideally in
Boise, Ada County, Idaho, 83701, USA
Listing for: Saransh Inc
Part Time, Remote/Work from Home position
Listed on 2026-10-30
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Architect, Software Engineer, Backend Developer
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below
Position: Lead AI .NET Developer (Remote) - Only W2
  • We are looking for a highly hands-on, execution-focused Lead AI Developer to serve as the ground-level technical partner to our Lead AI Engineer.
  • While the Lead AI Engineer establishes the architectural vision and transformation roadmap, you will be the core builder in the trenches writing production-grade C#/.NET code, engineering stateful agentic workflows, and implementing automated pipelines.
  • Your primary mission is to revolutionize our Software Development Life Cycle (SDLC) and supercharge developer productivity by building, tuning, and deploying agentic AI tools that seamlessly integrate into clients' modern Microsoft and Git Hub engineering ecosystem across multiple teams.
Role:
Lead AI .NET Developer

Location:

Remote (US) Job Type: W2 Contract Need Candidates from the Fintech domain Description Role Summary
  • We are looking for a highly hands-on, execution-focused Lead AI Developer to serve as the ground-level technical partner to our Lead AI Engineer.
  • While the Lead AI Engineer establishes the architectural vision and transformation roadmap, you will be the core builder in the trenches writing production-grade C#/.NET code, engineering stateful agentic workflows, and implementing automated pipelines.
  • Your primary mission is to revolutionize our Software Development Life Cycle (SDLC) and supercharge developer productivity by building, tuning, and deploying agentic AI tools that seamlessly integrate into clients' modern Microsoft and Git Hub engineering ecosystem across multiple teams.
Responsibilities
  • Hands-on Agentic SDLC Implementation:
    Build, test, and deploy stateful multi-agent workflows, tool/function-calling mechanisms, and structured LLM output parsers in C#/.NET to automate software development life cycle (SDLC) stages (requirements translation, automated code generation, and test suite creation).
  • Enterprise Spec-Driven Development Execution:
    Translate specs, JSON schemas, and structured technical specifications into machine-readable formats that drive autonomous multi-agent code and test generation across repositories.
  • Developer Productivity & Tooling Integration:
    Develop custom extensions, plugins, and automation scripts integrated into Git Hub Copilot, Git Hub Actions, and Microsoft developer tool chains to streamline developer workflows and reduce cognitive load.
  • Direct Engineering Team Uplift & Pair Programming:
    Work directly in the trenches with engineering teams through pair programming, live debugging, and hands‑on guidance to demonstrate how to effectively adopt agentic AI tools in daily development.
  • Token Optimization & Fin Ops Execution:
    Implement and fine‑tune technical token optimization strategies including context-window management, prompt caching, and hybrid routing between Small Language Models (SLMs) and frontier models to minimize operational token costs.
  • Guardrail Enforcement & Code Quality:
    Implement the engineering guardrails, security validations, and code‑quality parsers defined by the Lead AI Engineer to ensure safe, compliant, and production‑ready AI‑generated code.
  • Ground-Level Feedback Loop:
    Gather tactical, daily feedback from developers regarding workflow friction points and report back to the Lead AI Engineer to continuously refine prompt sets, agent loops, and developer experience (Dev Ex).
Requirements
  • Preferred Github Copilot (advanced) with GH-600/300 certification
  • Hands‑on C# & .NET Engineering Expertise:
    Strong, current coding proficiency in modern C# and .NET Core, with a deep understanding of Architecture, cloud‑native patterns, and building robust backend tooling.
  • Practical Agentic AI & LLM

    Experience:

    Proven hands‑on experience building production‑grade LLM applications, stateful agent execution loops, structured outputs (JSON mode/function…
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