Description

You will build and maintain agentic AI systems, RAG pipelines, and the underlying infrastructure that supports them.

This role is on-site.

Responsibilities

• Implement and maintain MCP servers, agent code, and APIs for model access and orchestration.
• Design agent behavior, workflows, safety guards, and grounding strategies to mitigate hallucinations and leakage.
• Build and optimize RAG workflows including document chunking, embeddings, vector indexing, and retrieval tuning.
• Containerize services with Docker, manage Kubernetes orchestration, and optimize autoscaling and resource requests.
• Ensure observability through logging, metrics, traces, and SLOs while creating runbooks to reduce MTTR.

Required Skills

• 5+ years of strong software engineering experience with Python or Node.js.
• 2+ years of experience with LLMs, prompt engineering, and agent frameworks.
• Practical experience implementing RAG: embeddings, vector DBs, and retrieval tuning.
• Experience with 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 experience with Google Cloud Platform services.
• Experience with observability, testing, and security best practices for distributed systems.

Preferred Skills

• Familiarity with vendor and open-source vector stores and embedding providers.
• Experience with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).

Key Skills

Education

Any Gradute