Artificial Intelligence Engineer

Agile Resources, Inc.; St. Louis (MO); On-site; USD 170,000 - 180,000; Full time

Job summary

A leading technology firm is seeking a Principal Engineer to redefine software building techniques. This hybrid position requires expertise in AI integration and a strong background in Object Oriented Programming with at least 7 years of experience. The successful candidate will design intelligent systems, ensure security compliance, and lead a high-performing team. Compensation ranges from $170K to $180K annually plus an annual incentive. Ideal candidates will have experience with Java, GCP, AWS, and GitHub Copilot.

Qualifications

• 7+ years of experience in Object Oriented Programming with Java/Spring Boot, Python, or Node.
• 3-5 years experience with platforms integrating Large Language Models.
• Proven track record with GitHub Actions and complex software delivery automation.

Responsibilities

• Design the framework for intelligent coding assistants.
• Integrate AI agents with mission-critical platforms.
• Establish centralized repositories for AI instructions.
• Implement security protocols for autonomous systems.
• Deploy observability stacks for monitoring agent performance.

Skills

Cloud-Native programming

AI integration

Microservices

Security management

Leadership

Tools

GitHub Copilot

Terraform

AWS

GCP

Job description

Principal Engineer (Agentic Systems)Location: Hybrid with 3 days per week onsite in St. Louis
Type: Direct/Full-Time/Perm
Compensation: $170K-$180K + 15% annual incentive plan
We are partnering with a global data and analytics powerhouse to hire a visionary leader who will redefine how software is built. This isn't just a role for a coder; it's a role for a pioneer ready to build a greenfield, autonomous 'operating system' for engineering teams. You will own the strategy for integrating advanced AI assistants into every phase of the delivery cycle, transforming a massive enterprise into an elite, agent-led organization.

Here’s What You’ll Be Doing

• Architecting the Agent Ecosystem: Design the overarching framework for intelligent coding assistants, focusing on automated action execution, high-fidelity context retrieval, and complex prompt engineering.
• Integrating the Toolchain: Build and maintain an advanced toolset using the Model Context Protocol (MCP) to connect AI agents with mission-critical platforms like ServiceNow, DataDog, and cloud infrastructure.
• Defining AI Personas: Establish a centralized, version-controlled repository of custom instructions and standards that shape the behavior, tone, and accuracy of your engineering agents.
• Securing the Autonomous Core: Implement rigorous security guardrails, code-driven RBAC, and 'human-in-the-loop' protocols to ensure all agentic actions remain safe and compliant.
• Optimizing with Data: Deploy a comprehensive observability stack to track agent effectiveness, setting and monitoring KPIs to continuously refine the system’s reliability and performance.

Here’s What Our Ideal Candidate Has

• Cloud-Native Seniority: 7+ years of deep technical OPP (Object Oriented Programing) experience in languages such as Java/Spring Boot, Python, or Node, and public cloud environments (specifically GCP or AWS), with a focus on Kubernetes and distributed microservices.
• Agentic System Mastery: At least 3–5 years of hands-on experience architecting platforms that integrate with Large Language Models (LLMs), with specific expertise in RAG and function-calling paradigms.
• GitHub Ecosystem Expertise: Advanced proficiency in GitHub Copilot, Actions, and APIs, coupled with a history of automating complex software delivery pipelines.
• Security & Systems Thinking: A strong background in identity management (IAM) and secure software development, viewing the entire SDLC as an interconnected, programmable system.
• Leadership Presence: Proven ability to mentor high-performing technical teams and simplify intricate architectural concepts for both executive stakeholders and engineering peers.

Keywords

Agentic Engineering, GenAI, LLM Orchestration, Model Context Protocol (MCP), GitHub Copilot, Generative AI, RAG, GCP, Vertex AI, BigQuery, Pub/Sub, Kubernetes, GKE, EKS, AWS, Bedrock, SageMaker, Java Spring Boot, Python, Golang, Infrastructure as Code, Terraform, Prompt Engineering, Prompt Ops, DevSecOps, Observability, AI SDKs.

U.S. Citizens, Green Card Holders, and those authorized to work in the U.S. for any employer will be considered.