Description

You will design, build, and operate server and agent workloads for AI systems.

Responsibilities

• Develop agentic AI patterns, prompt engineering solutions, and LLM integrations for production use.
• Own deployment, scaling, and cost-efficiency for services running on Kubernetes/Docker within Google Cloud with automated CI/CD.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, integrating vector stores and retrieval tooling.
• Implement and maintain core server and agent code, APIs, and SDKs for model access and orchestration.

Required Skills

• 5+ years of Software Engineering experience using Python or NodeJS, including system design and production services.
• 2+ years of experience with LLMs, prompt engineering, and agent frameworks.
• 2+ years of practical experience implementing RAG, including embeddings, vector DBs, and retrieval tuning.
• 2+ years of experience using LangChain patterns and toolchain telemetry (Langfuse or similar) for traceability.
• 5+ years of experience with Kubernetes, Docker, CI/CD, and infrastructure-as-code.
• 2+ years of practical experience with Google Cloud Platform services.
• 2+ years of experience with Observability, testing, and security best practices for distributed systems.
• 2+ years of experience evaluating and mitigating retrieval failures, hallucinations, and leakage risks in RAG systems.
• Familiarity with CI/CD pipelines such as Jenkins, GitHub Actions, GitLab CI, or ArgoCD.

Key Skills

Education

Bachelor's degree