Senior Software Engineer - AI Integration

Foresite•Overland Park, KS

About The Position

Foresite is looking for a Senior Software Engineer - AI Intergration with strong, hands-on AI experience to help shape how AI gets built into our platform. We are a Managed Security Services Provider (MSSP) building the next generation of AI-powered security operations. We're integrating agentic AI into the workflows our analysts and customers rely on every day — a force multiplier for detecting, investigating, and responding to threats faster.

Requirements

5+ years of software engineering experience with strong fundamentals (you write production code, not just notebooks).
Deep, practical experience using Agent Development Kit. You should have a proven track record of integrating LLMs into production environments - prompt engineering, context management, RAG architectures, agent frameworks, evaluation and observability.
Deeply proficient in React 18/19 and TypeScript. You are comfortable with React Router v7 and modern routing patterns.
Comfortable designing schemas, writing queries, and reasoning about performance across PostgreSQL and Firestore. You feel at home with modern ORMs and use runtime validation to enforce data integrity at system boundaries.
Experience designing secure multi-tenant AI architectures — you understand how to build LLM integrations that enforce strict data isolation between customers.
Strong understanding of scalable and highly available cloud-native system design.
Experience working closely with product teams to ship user-facing features.

Nice To Haves

Knowledge of Infrastructure as Code (IaC) using Terraform to automate, orchestrate, and manage cloud platform resources effectively.
Familiarity or hands-on experience with Google Cloud Security products (e.g., Security Command Center, SecOps, , Mandiant).
Experience building autonomous AI agents, multi-modal applications, or complex prompt orchestration pipelines.

Responsibilities

Design and build AI-powered workflows that serve both internal analysts and external customers across guided security operations
Own the AI architecture end-to-end: model selection, prompt engineering, RAG pipelines, agent orchestration, and evaluation frameworks
Optimize for cost and performance — manage token budgets, reduce latency, and build instrumentation so we know exactly what our AI spend buys us
Collaborate with product and engineering to identify where AI creates real leverage and where it doesn't
Mentor engineers on LLM best practices and raise the AI literacy of the broader team