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Job Description
We are seeking a Senior AI Engineer to join the Engineering Frameworks team and contribute to the development, integration, operationalization, and support of enterprise AI platforms and developer tools.
This is a hands-on engineering role focused on building AI capabilities that enable development teams across the organization to create and deploy solutions for their lines of business. The ideal candidate will have direct experience with AI/LLM technologies, GCP, Gemini, Vertex AI, Claude Code, GitHub Copilot, MCP, and agentic application frameworks, combined with strong Java/Spring Boot development and API integration skills.
The Senior AI Engineer will own significant technical work efforts from design through implementation and production support while helping establish reusable patterns for AI applications and developer tools.
Key Responsibilities
- Develop and maintain AI platforms, developer tools, frameworks, and shared services used by engineering teams.
- Build and integrate AI capabilities using GCP, Gemini, Vertex AI, Claude Code, GitHub Copilot, MCP, and related technologies.
- Develop reusable AI services, orchestration patterns, reference implementations, and integration components.
- Design and implement custom agent workflows, including sequential, multi-agent, and deterministic graph-based solutions.
- Build secure, scalable, reliable, observable, and maintainable AI services and integrations.
- Develop Java/Spring Boot services and REST APIs supporting AI platforms and applications.
- Integrate AI capabilities with enterprise cloud, security, identity, and technology platforms.
- Build or integrate MCP servers and clients that expose tools, resources, and data sources to AI agents.
- Provide technical support and guidance to development teams building and operating AI applications and agents.
- Implement logging, metrics, tracing, token utilization monitoring, and other operational capabilities for AI services.
- Apply security, privacy, access control, responsible AI, governance, and cost-management practices.
- Participate in code reviews, technical design discussions, troubleshooting, testing, and production support.
- Contribute to platform documentation, release management, adoption, and continuous improvement.
- Collaborate with engineering, architecture, security, product, and business teams to deliver AI platform capabilities.
- Share knowledge and mentor junior engineers and development teams on AI technologies and best practices.
Required Qualifications
- 5–7 years of experience in software engineering, platform engineering, AI engineering, or a related technical discipline.
- Proven experience delivering production-quality software solutions.
- Hands-on experience building or integrating AI, LLM, automation, or developer-platform capabilities.
- Experience with cloud-based AI platforms such as GCP, Gemini, and Vertex AI, or comparable technologies.
- Strong understanding of application architecture, APIs, integration patterns, and software design principles.
- Strong Java and Spring Boot development experience.
- Experience developing REST APIs and secure integrations.
- Experience with OAuth 2.0 / OpenID Connect, including service accounts, client credentials, or delegated user access.
- Understanding of CI/CD, observability, reliability, security, privacy, and responsible AI practices.
- Strong problem-solving, communication, collaboration, and technical documentation skills.
Preferred Qualifications
- Hands-on experience with Claude Code and GitHub Copilot, including custom instructions, prompts, skills, plugins, or extensions.
- Experience developing custom AI agents and reusable orchestration patterns.
- Experience with sequential, multi-agent, or deterministic graph-based agent workflows.
- Experience building or integrating MCP servers and clients.
- Experience with A2A or comparable agent-to-agent communication protocols.
- Experience with Google ADK, Spring AI, LangChain, LangChain4j, or similar AI/LLM frameworks.
- Experience with Gemini Agent Platform / Vertex AI capabilities.
- Experience implementing AI governance controls, token limits, rate limits, access policies, and cost controls.
- Experience implementing AI observability, including distributed tracing, token monitoring, metrics, and operational dashboards.
- Experience with containers, microservices, and cloud-native application architecture.
- Experience with Harness and Azure DevOps CI/CD pipelines.
- Familiarity with Agile/Scrum development practices.
Technical Skills
- GCP / Gemini / Vertex AI
- AI/LLM Integration
- AI Agent Development
- Claude Code / GitHub Copilot
- MCP Servers and Clients
- A2A / Agent-to-Agent Integration
- Google ADK / Spring AI / LangChain / LangChain4j
- Java / Spring Boot
- REST APIs
- React.js
- OAuth 2.0 / OpenID Connect
- Microservices / Cloud-Native Architecture
- CI/CD / Harness / Azure DevOps
- Logging / Metrics / Distributed Tracing
- AI Governance / Security / Responsible AI
Ideal Candidate
The ideal candidate is a hands-on senior engineer who can take AI capabilities from concept through production implementation. They should have practical experience integrating AI technologies into enterprise applications and be comfortable working across application development, APIs, cloud platforms, security, and operational support.
This person should be able to independently own significant technical deliverables, collaborate effectively with architects and engineering teams, troubleshoot complex technical issues, and help other developers adopt emerging AI tools and development patterns.