Artificial Intelligence Engineer

Agility Partners; Columbus (OH); On-site; USD 140,000 - 190,000; Full time

Job summary

Agility Partners seeks a Senior AI Engineer to join a Finance IT group focused on building and operating AI platforms and developer tools for engineers across the organization.

You will lead end-to-end efforts, own significant technical work, and collaborate with security, architecture, and product teams to deliver scalable AI capabilities such as cloud AI services and agentic workflows.

Qualifications

• 5-7 years of software/AI engineering experience or equivalent
• Experience delivering production-grade AI platforms and developer tools
• Hands-on with cloud AI services (GCP, Gemini, Vertex AI) and integration patterns
• Strong knowledge of secure API design, CI/CD, observability, and governance
• Experience with Java REST APIs and React.js a plus

Responsibilities

• Develop and maintain AI platforms, tools, and frameworks used by engineers
• Implement AI platform services, orchestration patterns, and shared integrations
• Design tools to support development of custom agents and workflows
• Integrate and operationalize AI tools with cloud, security, and enterprise platforms
• Build secure, scalable, observable, and reliable services for custom agents

Skills

GCP

Gemini

Vertex AI

Claude Code

GitHub Copilot

Java Spring Boot

React.js

OAuth 2.0/OpenID Connect

MCP servers

CI/CD

Education

Bachelor's degree in CS/Engineering

Tools

Spring Boot

Kubernetes

Job description

Our client, a large financial services organization, is seeking a talented Senior AI Engineer to join its Engineering Frameworks Team. In this role, you will contribute to the design, development, integration, operationalization, and support of AI tools and platforms used by developers across the organization to build solutions for various lines of business. This is a hands-on engineering role centered on cloud AI services (such as GCP, Gemini, and Vertex AI or comparable platforms), Claude Code, GitHub Copilot, MCP servers, Java Spring Boot, and React.js. The ideal candidate will have direct, demonstrable experience building and integrating with AI tools and services, along with the ability to deliver reliable, secure, and maintainable solutions that accelerate software development and improve productivity across the organization.

This senior role focuses on developing and supporting AI platforms, developer tools, and agentic application capabilities. It requires significant experience delivering high-quality software solutions, applying sound application and integration patterns, and contributing to the design of secure and scalable AI capabilities. The Senior AI Engineer will own significant technical work efforts end-to-end, collaborate with development teams and technical leaders, and help establish reusable patterns for building and operating custom agents throughout the organization.

Responsibilities

• Develop and maintain AI platforms, tools, and frameworks used by engineers across the organization, including Claude Code, GitHub Copilot, MCP servers, plugins, skills, and agentic frameworks


• Implement AI platform services, orchestration patterns, reference implementations, and shared integration components in collaboration with technical leads and architects


• Design and deliver tools that support development of custom agents and workflows, including sequential, multi-agent, and deterministic graph-based solutions


• 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 cloud infrastructure


• 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 as well as engineers on development teams, contributing to a culture of continuous improvement

The ideal candidate

Education

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

Basic Qualifications

• 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, 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, including service accounts, client credentials, or delegated user access


• 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

Preferred Qualifications

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


• Designing or implementing custom AI agents, reusable orchestration patterns, shared integrations, or reference implementations


• Building sequential, multi-agent, or deterministic graph-based agent workflows


• Building or integrating MCP servers and clients to expose tools, resources, or data sources to AI agents


• 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


• Experience with agent/model platform capabilities such as model-garden, model-runtime, model-gateway, or agent-gateway


• Implementing model governance controls such as model-armor tooling, cost controls, token limits, rate limits, and access policies


• Implementing logging, metrics, distributed tracing, token-utilization monitoring, and operational dashboards for AI services on cloud infrastructure


• Exposure to containerization, microservices, and cloud-native application architecture


• Experience working with CI/CD pipelines (e.g., Harness, Azure DevOps), including automated build, test, security, and release workflows


• Familiarity with Agile/Scrum methodologies and development practices for shared platform capabilities

Technical Skills

• AI Platforms: cloud AI services, 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, privacy, responsible AI, and access control


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

Strengths and Capabilities

• Strong, hands-on knowledge of AI/LLM integration, agent development, platform engineering, and enterprise software delivery


• Ability to build, integrate, operate, and support AI platforms and developer toolkits using leading cloud AI services, developer tools, and MCP-related technologies


• Ability to apply sound design patterns and contribute to reusable orchestration patterns, shared integration components, and maintainable frameworks


• Ability to own substantial work efforts end-to-end, make sound technical decisions, deliver high-quality solutions, and resolve complex implementation and integration issues


• Ability to apply secure-by-design, privacy, governance, access-control, cost-management, and responsible AI principles to enterprise AI solutions


• Ability to understand developer and business needs, provide useful feedback on platform capabilities, and contribute to continuous improvement


• Ability to work effectively with development teams, technical leads, product management, security, architecture, lines of business, and other stakeholders


• Ability to share knowledge, support junior engineers, and encourage effective adoption of AI tools, platforms, frameworks, and best practices


• Ability to learn and apply rapidly evolving AI technologies, developer tools, platform capabilities, and industry practices

Reasons to love it

• Join a growing Engineering Frameworks Team at the forefront of enterprise AI adoption within a large, established financial services organization


• Hands-on ownership of cutting-edge AI platform and agentic tooling initiatives, with direct impact on developer productivity across the company


• Opportunity to work with leading-edge technologies, including Claude Code, GitHub Copilot, MCP servers, and cloud-based AI/LLM platforms


• Collaborative environment working alongside technical leads, architects, security, and cross-functional development teams


• Meaningful mentorship opportunities, helping shape best practices and the technical growth of junior engineers and broader development teams


• Exposure to responsible AI, governance, and security practices at enterprise scale


• A role that blends deep technical ownership with strategic influence over reusable frameworks and organization-wide standards