Principal Ai

Principal AI-Dallas, Texas

Must have GCP experience

Interview process: 2 interview 1 with hiring manager and team and one with leadership

Contract to hire

The manager has 3 teams currently , 1 is a backend development (Java) , React and now this one that is AI

They will be responsible for helping to provide the tool sets to help engineers build the agents behind the scenes

Extensive background in software engineering development then moved in AI

The principal role ideally must have GCP and familiarity with Java Springboot . The senior and junior is not needed

Must haves : Claude code, agentic Ai, Gemini , GCP (principal) AWS is ok for other 2 , Google ADK, Langchain and SpringAi, MCP Servers are must as well.

Running and operating Claude code is a must , building agents themselves is a must

We are looking for a talented Principal AI Engineer to join our Engineering Frameworks Team. In this role, you will be instrumental in leading a team in the design, architecture, development, integration, operationalization, and support of AI tools and platforms utilized by developers across the company to build solutions for their lines of business. This is a hands-on engineering role centered on GCP, Gemini and Vertex AI (or comprable services), Claude Code, Copilot, MCP Servers, Java Spring Boot, and React.js. The ideal candidate will have direct, demonstrable experience building and integrating with these specific tools and services, not just general AI familiarity, and a passion for developing, maintaining, and advocating tools that accelerate software development, enhancing productivity and innovation across the organization.

Job Description

A lead role acting as a technical expert and advisor for enterprise AI engineering, developer platforms, and agentic application capabilities. This position requires deep, demonstrable expertise in designing, building, integrating, operationalizing, and supporting secure and scalable AI platforms and tools used by engineering teams across the company. The Principal AI Engineer drives strategic AI platform initiatives, establishes reusable architecture and orchestration patterns, influences the company s technical direction, and enables development teams to build and operate reliable custom agents.

Responsibilities

Serve as a technical expert and development leader for enterprise AI platforms, agent frameworks, and developer productivity capabilities.

Drive strategic initiatives involving GCP, Gemini, Vertex AI, Claude Code, GitHub Copilot, MCP servers, A2A integrations, and agentic frameworks.

Design the architecture and implementation of reusable AI platform services, orchestration patterns, reference implementations, and shared integration components.

Integrate and operationalize AI services with cloud, security, and enterprise technology platforms.

Establish secure, scalable, observable, performant, and maintainable patterns for custom agents and AI tool integrations.

Provide technical leadership, support, training, and guidance to engineering teams building, deploying, and operating custom agents.

Define framework lifecycle practices for versioning, release management, documentation, adoption, support, and continuous improvement.

Establish and promote responsible AI, privacy, access control, governance, cost management, and model-risk practices.

Lead the implementation of observability capabilities for logging, metrics, token utilization, tracing, and operational health on GCP-hosted services.

Partner with product management, security, architecture, and lines of business to shape the AI platform roadmap and align delivery with enterprise needs.

Mentor junior engineers, identify training needs, and lead the team through the end-to-end product development and delivery lifecycle.

Education

Bachelor s degree or equivalent work experience in computer science, engineering, artificial intelligence, or a related field

Basic Qualifications

7-10 years of experience in software engineering, platform engineering, AI engineering, or a related technical discipline

Proven experience designing and delivering production-quality AI, LLM, or developer-platform capabilities

Demonstrable hands-on experience with cloud-based AI services such as GCP Gemini and Vertex AI or comparable model platforms

Extensive knowledge of AI agent architecture, reusable orchestration patterns, shared integrations, and cross-team platform consumption

Strong experience with enterprise software development using Java and Spring Boot, and working knowledge of React.js for single-page applications

Experience designing and integrating secure REST APIs and OAuth 2.0 / OpenID Connect authorization flows, including service-account, client-credentials, or delegated-user patterns

Strong understanding of software development lifecycle, CI/CD, observability, reliability, security, privacy, and responsible AI practices

Preferred Qualifications

Hands-on experience using Claude Code and GitHub Copilot in an engineering workflow, including custom instructions, prompts, skills, plugins, or extensions

Experience building or integrating MCP servers and clients to expose tools, resources, or data sources to AI agents

Experience enabling agent-to-agent communication using A2A or comparable protocols

Experience with Google ADK, Spring AI, LangChain, LangChain4j, or similar LLM integration libraries and SDKs

Direct experience with Gemini Agent Platform / Vertex AI capabilities such as model garden, model runtime, model gateway, or agent gateway

Experience implementing model governance controls such as GCP Model Armor, cost controls, token limits, rate limits, and access policies

Experience designing logging, metrics, distributed tracing, token-utilization monitoring, and operational dashboards for AI services on GCP

Experience with containerization, microservices, and cloud-native application architecture

Experience designing and maintaining CI/CD pipelines in Harness and Azure DevOps, including automated build, test, security, and release workflows

Familiarity with Agile/Scrum methodologies and product management practices for shared platform capabilities

Technical Skills Required

AI Platforms: GCP, Gemini, Vertex AI, Gemini Agent Platform, model garden, model runtime, model gateway, and agent gateway

AI Engineering: custom agent design, sequential workflows, multi-agent systems, deterministic graph-based agents, and reusable orchestration patterns

Developer Tools: Claude Code, GitHub Copilot, custom instructions, prompts, skills, plugins, and extensions

Agent Integration: MCP servers and clients, tools, resources, data-source integrations, A2A patterns, and agent-to-agent communication

Frameworks: Google ADK, Spring AI, LangChain, LangChain4j, or comparable AI/LLM integration libraries

Application Development: Java, Spring Boot, REST APIs, React.js, microservices, and cloud-native application patterns

Security and Governance: OAuth 2.0, OpenID Connect, service accounts, delegated access, model governance, GCP Model Armor, privacy, responsible AI, and access control

Operations: CI/CD, Harness, Azure DevOps, logging, metrics, tracing, token utilization, performance, reliability, and incident support

Strengths and Capabilities

Technical Expertise: Deep, demonstrable expertise in AI/LLM integration, agent architecture, platform engineering, and enterprise software delivery.

Platform Proficiency: Ability to design, integrate, operate, and support shared AI platforms and developer toolkits on GCP using Gemini, Vertex AI, Claude Code, GitHub Copilot, MCP, and related technologies.

Architecture and Design: Ability to create reusable orchestration patterns, reference implementations, shared integration components, and scalable frameworks that enable engineering teams to build custom agents.