AI Engineer

Responsibilities

• Build and deploy production AI features: LLM-backed workflows, retrieval-augmented generation, agentic and tool-using systems, classification and extraction pipelines, and traditional ML models where they're the better fit.
• Own the data path behind those features: ingestion, chunking, embedding, indexing, and retrieval quality, and improve it based on measured outcomes rather than intuition.
• Design evaluation before you ship. Define what "good" means for each use case, build offline eval sets and online feedback loops, and track regressions as prompts, models, and data change.
• Integrate AI capabilities into existing applications and services through well-designed APIs, with attention to latency, cost per request, failure modes, and graceful degradation.
• Instrument and monitor deployed systems for quality drift, hallucination rates, token spend, and abuse, and act on what you see.
• Apply responsible-AI practices in day-to-day work: data privacy and residency, PII handling, prompt injection and data exfiltration risks, access control, and clear documentation of model limitations.
• Prototype quickly to reduce uncertainty, then make a deliberate call on what deserves to be hardened and what should be thrown away.
• Contribute to internal standards, reusable components, reference architectures, and patterns other teams can adopt.
• Explain technical tradeoffs to non-technical audiences, including client stakeholders and executives, without overselling what the technology can do.

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