Senior Software Engineer, AI Platform

isolved is a leading provider of human capital management (HCM) solutions that combines modern technology with expert services and support. The Senior Software Engineer, AI Platform role owns both program execution and technical direction, leading a team of engineers and serving as a key decision-maker in delivering AI-powered systems.

Responsibilities:

• Design and build a scalable LLM gateway with model routing, prompt management, cost attribution, rate limiting, and caching
• Develop and operate RAG pipelines, embedding services, and vector search infrastructure for platform-wide use
• Implement platform-level cost optimization strategies, including semantic caching and model selection by workload
• Build and maintain agentic runtime infrastructure, including orchestration, state management, and human-in-the-loop patterns
• Develop extensible MCP server and tool ecosystems for product team integration
• Design and support multi-agent coordination patterns using modern frameworks and protocols
• Establish comprehensive AI observability, including usage, latency, cost tracking, and distributed tracing
• Implement AI governance controls, including access management, audit logging, content filtering, and security protections
• Build AI incident detection and response capabilities, including monitoring for failures, hallucinations, and cost anomalies
• Create developer-friendly SDKs across languages (Python, .NET, TypeScript) to simplify platform adoption
• Define 'paved road' patterns for common AI use cases and support onboarding of product teams
• Build automated evaluation pipelines and continuously monitor production quality and model performance

Requirements:

• Remote | U.S.-Based Candidates Only
• 5+ years of professional software engineering experience, with Python as your primary language
• 2+ years building production LLM-powered systems - inference, RAG, agentic patterns, or AI infrastructure
• Deep Python expertise - this is the primary language for AI platform work
• Working proficiency in C#/.NET - the platform serves teams that live in C#, so interop is real and matters
• Strong hands-on experience with agentic frameworks - Semantic Kernel, LangGraph, LangChain, or you've built your own
• Production experience with RAG architecture: chunking strategies, embedding models, vector search, retrieval quality, and the failure modes that don't show up in demos
• Azure AI Foundry / Azure OpenAI experience - model deployment, API integration, observability tooling
• Experience building internal platforms or SDKs that other engineers depend on - you understand what makes a platform feel good to use
• Strong grasp of AI observability: token usage, latency, cost tracking, and distributed tracing across multi-agent workflows
• Experience with TypeScript and building developer SDKs or tooling
• Hands-on experience with AI evaluation frameworks (LLM-as-judge, automated regression testing)
• Knowledge of AI governance practices, including access control, audit logging, and security safeguards
• Familiarity with container-based deployments (e.g., Azure Container Apps) and infrastructure-as-code (Terraform)
• Awareness of AI regulatory frameworks such as NIST AI RMF or ISO/IEC 42001