Client of Salt
Senior AI / Automation Engineer
Skills
1 month ago
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Important information
Location:
Abu Dhabi - United Arab Emirates
Work mode:
Onsite
Published on:
13 September 2026
What they need
Our client is looking for a Senior AI / Automation Engineer to build and productionise AI-powered enterprise workflows. This is not a chatbot or RAG-focused role . The ideal candidate will combine strong software engineering with hands-on experience in LLMs, agentic AI, document intelligence and enterprise automation . Key Responsibilities: Build and deploy production LLM and agentic AI workflows . Develop tool-calling agents integrated with enterprise APIs and systems. Build document extraction, classification and reasoning solutions. Implement RAG, context management and structured outputs. Integrate AI with deterministic enterprise workflows. Build guardrails, validation, confidence thresholds and human-in-the-loop controls . Develop automated AI evaluation and regression testing. Implement model routing, fallback strategies and inference-cost controls. Monitor AI quality, latency, token usage and failure rates. Determine when AI should be used versus deterministic software.
Desired Candidate Profile
- Strong software engineering background with production AI/LLM experience .
- Advanced Python and API/service development skills.
- Hands-on experience with RAG, embeddings, tool/function calling and structured outputs .
- Experience building asynchronous, stateful and multi-step workflows.
- Strong understanding of testing, observability and production reliability.
- Experience with LLM evaluation, model selection and inference economics .
- Proven track record taking AI solutions from prototype to production .
- Strong Advantage Experience with: Agent orchestration and multi-agent systems Enterprise document processing and OCR AI automation across Finance, Procurement, HR or Legal Multi-model architectures Enterprise security, data residency and regulated environments
- The key mindset: build AI that works reliably in the real world including knowing what to do when the model is wrong
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