AI Engineer

Overview

Key Responsibilities

• Agent graph development: Design and implement LangGraph agent graphs for Supervisor-Worker orchestration, self-reflection, actor-critic, and HITL interrupt patterns using TypedDict state schemas and conditional edge routing.
• LLM extraction pipeline: Build and maintain LLM-based entity extraction pipelines using Azure AI Foundry, implementing structured output enforcement, per-field confidence scoring, and Pydantic output validation against Use Case Manifest schemas.
• Prompt engineering and governance: Author, version, and govern prompts through the AWA Prompt Management System (PMS) using semantic versioning; run prompt sensitivity and correctness testing using the AWA IDP Test Strategy.
• MCP tool integration: Develop and maintain the five core MCP tool servers (query_structured, retrieve_precedents, query_documents, checkpoint_state, observe) and extend them for new use case requirements.
• Model evaluation and testing: Execute Band 1 and Band 2 testing (Model Testing, Prompt Testing, Agent Testing, AI Security Testing, Adversarial Testing) per the AWA IDP Test Strategy; maintain the Golden Dataset and Regression Dataset.
• HITL framework: Implement HITL interrupt() calls at mandatory and dynamic review points in LangGraph graphs; configure Orkes Wait tasks; validate HITL state persistence and checkpoint-based resumption.
• AI guardrail integration: Integrate Azure AI Content Safety, PII detection and tokenisation, prompt injection shielding, and output sanitisation into agent pipelines at the AWA AI Gateway layer.
• Cross-field validation rules: Implement business validation rules (amount-words-vs-figures, date validity, MICR format, referential integrity) within LangGraph validation agent nodes.

Required Skills and Experience

• Technical — Essential
• 5+ years Python development — production-grade, not just scripting
• Hands-on experience with LangGraph, LangChain, or equivalent agentic AI frameworks
• Practical LLM prompt engineering: structured outputs, few-shot design, output schema enforcement
• Familiarity with Azure AI Foundry / OpenAI API or equivalent LLM API (Anthropic, Cohere)
• Understanding of agent design patterns: ReAct, self-reflection, tool use, HITL
• REST API development and consumption; JSON schema design
• Git version control; CI/CD pipeline awareness
• Technical — Advantageous
• Experience with Orkes Conductor, Apache Airflow, or Temporal for workflow orchestration
• Azure AI Document Intelligence or equivalent OCR platform experience
• Computer vision model integration (object detection APIs)
• Knowledge of RAG architectures: chunking, vector indexing, Azure AI Search
• Azure platform familiarity: AKS, ADLS Gen2, Azure ML, Azure Monitor

Domain and Soft Skills

• Genuine intellectual curiosity about AI systems and their failure modes
• Ability to explain AI behaviour to non-technical Ops and business stakeholders
• Structured problem-solving and debugging approach for non-deterministic AI systems
• Banking domain interest — willingness to deeply understand the business processes being automated

Qualifications

• Bachelor's degree in Computer Science, AI/ML, Software Engineering, or related field
• Master's degree or equivalent research/industry experience advantageous
• Relevant Azure certifications (AI Engineer Associate, Developer Associate) advantageous
• Pay: AED18,000.00 - AED24,000.00 per month