Senior Backend Engineer - AI Enablement

Senior Backend Engineer AI Enablement (MCP Infrastructure Channel Adapters)

About the Role

We are building an AI Enablement team focused on delivering a robust platform that empowers product and engineering teams to integrate AI capabilities rapidly, safely, and at scale. As a Senior Backend Engineer, you will be responsible for designing and implementing core components of our Model Context Protocol (MCP) infrastructure, building channel adapters, and enabling seamless integration with Large Language Model (LLM) tools and workflows.

This role requires a strong engineering foundation, a platform-team mindset, and the ability to build highquality, testfirst systems that other teams can depend on.

Key Responsibilities

Platform Engineering MCP Infrastructure

• Design, develop, and maintain backend services powering the MCP platform including tool registries, adapters, execution runtimes, and orchestration layers.
• Build and evolve channel adapters that connect AI systems to external data sources, APIs, and enterprise services.
• Ensure reliability, observability, and operational excellence across all components.

Test-First Engineering

• Practice stricttest-first development: TDD by default, with high-quality unit, integration, and contract tests.
• Drive engineering rigor and promote test-first culture within the team.

LLM Tooling Integration

• Apply practical understanding of LLM fundamentalstokens, context windows, tool descriptions, prompt structuresto build effective and efficient integrations.
• Implement and optimize MCP tool designs that allow other teams to create and consume AI functionality safely.

Platform Mindset Collaboration

• Work closely with cross-functional engineering groups, understanding their needs and enabling them to deliver faster.
• Measure success bywhat other teams ship through the platform, not just what the AI Enablement team builds.
• Contribute to documentation, platform SDKs, and examples that lower adoption friction.

Required Qualifications

• Strong expertise in .NET(C#), with deep knowledge of backend development patterns, async programming, networking, and distributed systems.
• Proven experience delivering production-grade platforms, APIs, or high-scale services.
• Test-First mindset: TDD, contract testing, mocking strategies, and CI-driven quality gates.
• Working knowledge of LLMsincluding:
  • Tokens context window limits
  • Tool descriptions invocation patterns
  • Prompt and response schema design
• Ability to build quickly and iterate without requiring deep theoretical AI expertise.
• Excellent collaboration skills with a service-oriented mindset.

What Success Looks Like

• Other teams ship AI-powered featuresfaster and more safelybecause of the infrastructure you build.
• AI tools are easy to integrate, observable, testable, and reliable.
• MCP-based components scale seamlessly across multiple teams and use cases.
• Engineering quality remains consistently high because of your test-first approach.

Preferred Qualifications

• Experience with Model Context Protocol (MCP) or similar tool/agent frameworks.
• Familiarity with event-driven architectures, streaming platforms, or asynchronous pipelines.
• Exposure to AI ecosystems: vector stores, embeddings, RAG, or agent frameworks.
• Experience building internal developer platforms or enablement tooling.