Applied Ai Engineer

About This Role

**HIRING**

Job Title - Applied AI Engineer

Contract Length - 12 Months Contract with view to extend

Location – Abu Dhabi

Start Date – ASAP – Subject to Security Clerance

About the Team

My client is building a Data & AI team to join a product and platform execution team — not a traditional enterprise data function. They build and operate AI-enabled systems that create measurable operational impact across the organization. The primary output is working software in production.

What you will be doing

Build, own, and continuously improve the AI capabilities across the office's production systems, working under the direction of the Principal AI Engineer.

Your scope: automated data classification (remediating what's currently broken), AI-generated briefings, and operational AI agents that deliver real value in weeks.

An external partner is building an agentic platform that the client will use. Your role in that relationship: define the data contracts the platform consumes, evaluate the sample agents delivered, and build the production agents on top of the platform that go beyond demonstrations into real operational use. You don't build the platform — you make sure the data is ready for it and that what gets built on top of it actually works.

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)

Thanks,

Ollie