AI/ML Agent Engineer — Back-End Focus
Indexed description
Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.
Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.
Role Overview
We’re hiring a Junior AI/ML Agent Engineer with a back-end focus to build reliable agent services and APIs using Java Spring Boot. You’ll implement agentic workflows powered by LLMs, OpenAI specs, and MCP, integrating with developer tools like GitHub, Cline, and Claude models.
Key Responsibilities
- Develop Spring Boot microservices that orchestrate LLM-powered agents using OpenAI specs and MCP (clients/servers, tool schemas).
- Implement tool execution layers, function calling, retries, guardrails, and safety checks for robust agent behavior.
- Build APIs and event-driven pipelines for agent requests, context assembly (RAG/embeddings), and result streaming.
- Integrate with GitHub (webhooks, Actions, PR review bots), Cline workflows, and Claude endpoints for developer-focused automations.
- Manage configuration, secrets, and access control; ensure compliance with internal security standards and least-privilege design.
- Add observability: logs, metrics, traces, and run metadata; create dashboards for reliability and performance.
- Write comprehensive tests (unit/integration), automate builds/deployments, and maintain documentation for specs and service contracts.
- Collaborate with front-end and platform teams to deliver end-to-end features; participate in design and code reviews.
- 2–5 years of professional experience in back-end engineering with Java and Spring Boot.
- Practical experience integrating LLMs (OpenAI, Claude) into services; understanding of agentic workflows and tool/function orchestration.
- Familiarity with MCP and implementation of clients/servers, plus OpenAI spec-driven tool definitions.
- Experience with REST/GraphQL APIs, data modeling, and message/event systems (e.g., Kafka/SQS).
- Exposure to GitHub APIs/Actions, Cline, and Claude model integrations.
- Solid grasp of testing, CI/CD, and cloud deployment patterns.
- Experience with RAG, embeddings, or lightweight vector stores; caching and rate-limiting for cost/perf control.
- Knowledge of security for AI services: prompt injection defenses, input validation, and secret management.
- Observability with Datadog/OpenTelemetry or similar; performance tuning in JVM/Spring.
- Eagerness to learn and adapt to new models and agent frameworks.
- Ownership mindset, attention to reliability, and clear communication.
- Collaborative approach across product, security, and platform engineering.
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