Senior AI Engineer - Hybrid

Genesis
10 is currently seeking a Senior AI Engineer for a contract to hire position with a Regional Financial Institution located in Columbus, OH. This is a 3+ month contract-to-hire opportunity. Candidates can also be located in:
Charlotte, NC;
Atlanta, GA:
Chicago, IL;
Minnetonka, MN;
Detroit, MI;
Dallas, TX. Compensation: $65.00 - $75.00 per hour, W2, based on qualifications

Position Overview:

We are looking for a talented Senior AI Engineer to contribute to the design, development, and support of AI tools and platforms for developers across the company. This is a hands-on engineering role centered on GCP, Gemini, Vertex AI, Java Spring Boot, and React.js. The ideal candidate will have demonstrable experience building and integrating with AI tools and services to deliver reliable, secure, and maintainable solutions that accelerate software development.

Key Responsibilities:

Develop and maintain AI platforms, tools, and frameworks used by engineers across the company, including Claude Code, Git Hub Copilot, MCP servers, plugins, skills, and agentic frameworks Implement AI platform services, orchestration patterns, reference implementations, and shared integration components Design and deliver tools that support the development of custom agents and workflows Integrate and operationalize AI tools and services with cloud, security, development, and enterprise technology platforms Build secure, scalable, reliable, observable, performant, and maintainable services for custom agents and AI tool integrations Provide technical support and guidance to development teams building, deploying, and operating custom agents and MCP servers Contribute to framework lifecycle practices, including versioning, release management, documentation, adoption, support, and continuous improvement Implement logging, metrics, token-utilization monitoring, tracing, and other operational capabilities for AI services running on GCP Apply enterprise security, privacy, responsible AI, access-control, governance, cost-management, and model-risk practices to AI solutions Collaborate with development teams, security, and architecture to define requirements and deliver platform capabilities Participate in code reviews, technical discussions, troubleshooting, and knowledge-sharing activities Mentor junior engineers on the team and on development teams, contributing to a culture of continuous improvement Primary Requirements:
Bachelor's degree or equivalent work experience in computer science, engineering, artificial intelligence, or a related field 5-7+ years of experience in software engineering, platform engineering, AI engineering, or a related technical discipline Proven experience delivering high-quality software solutions in production environments Hands-on experience building or integrating AI, LLM, developer-platform, or automation capabilities

Experience with cloud-based AI services platforms such as GCP/AWS Gemini, and Vertex AI or comparable model platforms Solid understanding of application architecture, integration patterns, APIs, and software design principles Experience developing Java REST APIs and services with Spring Boot and working knowledge of React.js or comparable front-end technologies

Experience with secure API integrations and OAuth 2.0 / OpenID Connect authorization patterns Understanding of the software development lifecycle, CI/CD, observability, reliability, security, privacy, and responsible AI practices Strong problem-solving skills, attention to detail, technical communication, and ability to collaborate effectively across teams AI Platforms: GCP/AWS Cloud, Gemini, Vertex AI, 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, Git Hub 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, Lang Chain, 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,…