AI Automation Engineer

Role overview A remote, full-time role focused on building AI-assisted systems that work reliably in production, not just in demos. The work spans retrieval pipelines, evaluation harnesses, agent workflows, integrations with business tools, and the guardrails that keep everything dependable. It suits an engineer who enjoys turning messy operational problems into measured, automated outcomes backed by solid engineering practice.

Responsibilities - Build LLM applications, retrieval pipelines, and workflow automations. - Design evaluations covering quality, latency, cost, and likely failure modes. - Integrate AI systems with business tools, external APIs, and structured data sources. - Create monitoring and fallback strategies for production workflows. - Explain system behavior and limitations to both technical and nontechnical teams.

Requirements - Strong Python skills and experience building production services. - Hands-on experience with LLM APIs, embeddings, and vector search. - Understanding of prompt evaluation, retrieval quality, and structured outputs. - Experience integrating APIs and designing asynchronous workflows. - A practical, grounded approach to privacy, security, and model limitations.

Nice to have - Experience with orchestration tools such as LangChain or LangGraph. - Knowledge of voice AI, document processing, or computer vision. - Familiarity with cloud queues and event-driven systems.

Benefits and work setup - Remote-first collaboration model. - Direct ownership of automation projects end to end. - Learning opportunities across modern AI and product stacks. - Flexible, outcome-focused work environment.