Description

Build and scale LLM-powered applications, agentic workflows, and evaluation systems for large-scale AI deployments.

Responsibilities

• Build and deploy LLM-powered applications and agentic workflows in production environments.
• Design automated evaluation frameworks to measure LLM performance, detect regressions, and handle non-determinism.
• Develop distributed systems for large-scale inference, optimizing for latency, cost, and reliability.
• Implement guardrails, fallbacks, and monitoring for AI systems, including streaming pipelines and async orchestration.
• Integrate AI/ML models into scalable backend systems and run experiments to validate improvements.

Required Skills

• 5+ years of experience in backend or distributed systems engineering.
• Strong programming skills in Python.
• Working knowledge of Go, TypeScript, or Rust.
• Hands-on experience shipping production AI/LLM systems at scale.
• Experience building agentic systems, including agents, tools, and workflows.
• Understanding of token limits, cost optimization, prompt design, and structured outputs.
• Knowledge of API rate limits, failure handling, and LLM evaluation challenges.
• Strong knowledge of AWS, GCP, or Azure.
• Hands-on experience with Docker and Kubernetes.

Preferred Skills

• Experience with agent orchestration frameworks (Lang Graph, Crew AI) and ML observability tools (Langfuse, Arize, W&B).
• Familiarity with retrieval systems (vector search, hybrid search) and streaming inference.

Key Skills

Education

Any Graduate