Job description

Why Work Here • Headquarters is in Charlotte, NC • Recently expanded to nearshore • isolved ranked for SMB Payroll in 2023 Sapient Report • Voted top places to work in USA 2023 Job Description The isolved Senior Software Engineer, AI Platform role owns both program execution and technical direction, leading ~20 engineers across domain teams (Tax, Benefits, Time, Payroll, Shared Logic), alongside two Engineering Managers and a Data Architect. The position blends delivery leadership with deep technical involvement, serving as a key decision-maker, escalation point for complex challenges, and ultimate owner of program outcomes. Core Job Duties • 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 Minimum Qualifications • • 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 • Preferred: • 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 About isolved isolved is a leading provider of human capital management (HCM) solutions that combines modern technology with expert services and support. Purpose-built for People Heroes™, isolved gives HR, payroll and benefits leaders the tools and insights to streamline operations and deliver employee experiences that matter. isolved People Cloud™ is a connected HCM platform with built-in artificial intelligence (AI) and analytics that brings together HR, payroll, benefits, workforce management and talent management in one experience. Built on a legacy of 40 years in the market, isolved is trusted by more than 200,000 employers and used by 9 million U.S. employees, representing about one in 20 American workers. Visit www.isolvedhcm.com. #LI-KJ1

Requirements

Must-haves

Core Job Duties
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
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

Nice-to-haves

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

Application prep

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Keywords to mirror

aillmpythontypescriptazureapi

Platform: isolved
Effort: medium