Sr AI Engineer
Posted Updated
Description
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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Skills
- AI
- API
- AWS
- Azure
- Azure DevOps
- Azure Monitor
- CI/CD
- Cloud
- Containerization
- Databricks
- Deep Learning
- DevOps
- Distributed Computing
- Docker
- Embeddings
- FAISS
- FastAPI
- Feature Engineering
- Flask
- GCP
- Generative AI
- Git
- Grafana
- Hugging Face
- Infrastructure as Code
- Keras
- Kubeflow
- Kubernetes
- LangChain
- LlamaIndex
- LLM
- Machine Learning
- MCP
- Milvus
- MLflow
- MLOps
- Model Deployment
- Neo4j
- NLP
- NumPy
- Observability
- OpenAI
- OpenAPI
- Oracle
- pandas
- pgvector
- Prometheus
- Python
- PyTorch
- Qdrant
- RAG
- SageMaker
- scikit-learn
- Snowflake
- Solution Design
- Spark
- SQL
- Statistics
- TensorFlow
- Version Control