Applied Ai Engineer

Overview

Job Title - Applied Ai Engineer

Location – Abu Dhabi

What This Role Owns

• Data classification automation
• : implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance Lead

- Operational Ai Agents

• : building production agents on top of the agentic platform — going beyond the sample agents the external partner delivers into real operational workflows
• **Agentic platform data contracts:**
• defining what data the platform needs, in what format, with what quality guarantees — working with the Principal AI Engineer
• AI service implementation:
• Fast API service around LLM APIs with versioned prompt templates
• Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution
• Prompt versioning:
• templates in configuration, editable without code changes

- Observability

• every LLM call logged with input hash, model version, output, latency, token count
• Fallback logic:
• graceful degradation when LLM APIs are unavailable
• Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts

Key Decisions

• **Prompt implementation**
• **Context window packing**
• Fallback behavior when APIs degrade
• **Agent evaluation**
• **Bug triage for AI components**

Required Skills & Technologies

• LLM APIs (Claude, GPT-4, open-weight models) — structured output, JSON mode, system prompts
• Prompt engineering for classification
• Python
• LLM evaluation
• Structured output — JSON schema enforcement, Pydantic validation
• Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)
• Token budgeting and context window management
• AI observability — output quality monitoring, anomaly detection
• FastAPI and Docker

Ideal Candidate

• Has shipped an LLM-based feature that non-technical users depend on daily — and has been responsible when it breaks.
• Knows the hardest part of applied AI is the fallback, the observability, and the human review loop — not the prompt.
• Can evaluate someone else's agent demo and quickly identify whether it's production-ready or held together with string.
• Works well under a senior AI lead — takes architectural direction and executes with high quality and speed.
• Not attached to a particular model — the job is reliable output.
• In this role you do not do - Not Looking for the below
• Define AI architecture or agent design patterns (Principal AI Engineer)
• Build the agentic platform itself (external partner)
• Build the backend API or data pipelines
• Fine-tune or train models
• Define governance policies (Governance Lead)