Description

You will design, build, and operate AI/ML infrastructure and agentic systems.

Responsibilities

• Design, build, and operate MCP servers and agents to host, orchestrate, and monitor AI/agent workloads.
• Develop agentic AI patterns, prompt engineering, and LLM integrations for production.
• Own deployment, scaling, reliability, and cost-efficiency on Kubernetes/Docker and Google Cloud via automated CI/CD.
• Design and implement RAG pipelines, integrating vector stores and retrieval tooling using LangChain and Langfuse.
• Implement and maintain MCP server/agent code, APIs, and SDKs for model access and orchestration.

Required Skills

• 5+ years of strong Software Engineering experience in Python or NodeJS, 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 with LangChain patterns and toolchain telemetry (Langfuse or similar).
• 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/augmentation failures in RAG systems.
• Familiarity with vendor and open-source vector stores and embedding providers.

Key Skills

Education

Any Graduate