Senior AI Platform Engineer: RAG, LLM & Observability

isolved; United States; Hybrid; USD 140,000 - 190,000; Full time

Job summary

isolved Senior Software Engineer, AI Platform leads a 20-engineer group across Tax, Benefits, Time, Payroll and Shared Logic, driving both delivery and technical direction. You will own program outcomes, serve as an escalation point for complex challenges, and define scalable AI platform capabilities that support product teams.

You will design, build, and operate multi-agent workflows, uphold observability and governance, and partner with managers and a Data Architect to ensure reliable platform

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.
• 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

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.

Skills

Python

LLM-powered systems

C# / .NET

Agentic frameworks

AI observability

TypeScript

AI governance

DevOps / CI-CD

Tools

Azure AI Foundry

Azure OpenAI

LangChain

Semantic Kernel

LangGraph

Vector search

Terraform

Docker

Job description

isolved Senior Software Engineer, AI Platform leads a 20-engineer group across Tax, Benefits, Time, Payroll and Shared Logic, driving both delivery and technical direction. You will own program outcomes, serve as an escalation point for complex challenges, and define scalable AI platform capabilities that support product teams.

You will design, build, and operate multi-agent workflows, uphold observability and governance, and partner with managers and a Data Architect to ensure reliable platform