Sr AI Engineer

Experience building agent orchestration or tool-calling frameworks in production; Experience with evaluation and quality measurement for generative systems (groundedness, citation accuracy, regression testing); Experience integrating across heterogeneous enterprise data estates and platforms owned by other teams; Experience in working with large datasets and distributed computing; Experience with cloud-based AI platforms (Azure AI, AWS SageMaker, GCP AI) preferred; Experience in enterprise or regulated environments (banking, financial services) is an advantage; Experience handling Arabic and English content is desirable.

Required Skills Machine Learning , Deep Learning , Natural Language Processing ( NLP ), GenAI/LLMs, Retrieval- Augmented Generation, Information Retrieval and Hybrid Search, Document Understanding and Chunking Strategies, Embeddings and Re-ranking , Knowledge Graphs, Ontologies and Semantic Layers, Agent Orchestration and Tool Calling, API and MCP Design, Python Programming , Feature Engineering , Data and Ingestion Pipelines, Prompt and Context Engineering, Model Deployment, MLOps , Evaluation and Benchmarking , Guardrails and Content Safety, Access Control and Security Trimming, Data Analysis, Statistics, Model Optimization, Model Monitoring, Observability, Hybrid and On-Premises Deployment, Containerisation and CI/CD, API Integration, Responsible AI, Explainability Techniques, Solution Design and Documentation, Problem Solving, Collaboration Preferred Tools and Certifications Programming: Python, SQL Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras GenAI Tools: OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, Model Context Protocol (MCP)

Retrieval and Vector: Azure AI Search, Oracle vector capabilities, open-source vector stores (for example FAISS, Milvus, Qdrant, pgvector) Graph: Neo4j or equivalent Document Processing: OCR and layout-aware parsing tooling, Arabic and English text processing Evaluation: RAG evaluation frameworks (for example RAGAS or equivalent) and custom golden-set harnesses Data Processing: Pandas, NumPy, Spark Data Platforms: Databricks, Snowflake MLOps : MLflow, Kubeflow, Azure ML; Cloud Platforms: Azure (preferred), AWS, GCP Deployment: Docker, Kubernetes, CI/CD, Infrastructure as Code; Version Control: Git, Azure DevOps API Tools: FastAPI, Flask, OpenAPI Monitoring: Prometheus, Grafana, Azure Monitor Certifications

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