AI Engineer

Role overview Build and operate production AI systems inside client environments, including retrieval-augmented generation, multi-agent applications, and workflow orchestration. The work spans integration with established business systems, evaluation and cost control, incident response, and helping client teams understand and use what is delivered.

Responsibilities - Implement AI agents and pipelines that connect to client CRM, ERP, and other existing tools, including systems with incomplete or inconsistent data. - Decide when data problems should be corrected in the source system and when they can be handled in code; document the reasoning. - Ship production systems such as RAG pipelines, multi-agent applications, and orchestrated workflows. - Create evaluation sets and regression thresholds before adding features, so changes can be assessed consistently. - Monitor operating costs and manage model selection and caching to keep production use within budget. - Respond to production incidents and restore service when systems fail. - Explain system behavior and practical adjustments to client teams without technical backgrounds.

Requirements - Ability to build and support software that runs in production, rather than stopping at prototypes. - Experience or practical capability with RAG, multi-agent systems, or AI workflow orchestration. - Working familiarity with Python or TypeScript and tools such as LangChain and Claude-based development tools. - Ability to integrate software with established business systems and make pragmatic decisions about data quality. - Skill in evaluating variable AI outputs and setting measurable quality checks. - Readiness to take responsibility for operational reliability, cost management, and incident response.