AI Engineer

About Commercial Bank of Qatar

Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar’s economic development by offering a range of personal, business, government, international and investment services.

We believe in empowering our employees, providing them with opportunities for growth and professional development.

By Joining us, you’ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.

Join us in shaping the future of banking!

The AI Engineer is responsible for designing, developing, and deploying Agentic AI solutions and AI-enabled platforms within the Data & AI Lab. This role focuses on building robust AI infrastructure including integrations with cloud platforms, vector stores, document processing pipelines, and real-time data streaming capabilities.

The position requires strong engineering skills to work with AI orchestration frameworks (MCP), cloud services (Azure, GCP), and modern AI tooling. The AI Engineer will collaborate closely with AI Product Owners, Data Scientists, and other engineers to deliver production-grade AI solutions that support banking operations and customer-facing applications.

The role bridges AI research and production deployment, ensuring that AI capabilities are scalable, maintainable, and aligned with enterprise architecture standards.

• Agentic AI Development
• Design and implement Agentic AI solutions including autonomous workflows, multi-agent systems, and AI orchestration patterns.
• Develop and maintain integrations with Model Context Protocol (MCP) and similar AI orchestration frameworks.
• Build and optimize AI pipelines that combine multiple AI capabilities into cohesive solutions.
• AI Platform Engineering
• Develop and maintain AI platform components including vector stores, embedding pipelines, and retrieval systems.
• Implement document parsing, processing, and metadata extraction pipelines for knowledge management and RAG applications.
• Build and maintain APIs and integration layers for AI services consumption across banking applications.
• Cloud & Infrastructure
• Design and deploy AI solutions on cloud platforms (Azure, GCP) following best practices for scalability and security.
• Implement data streaming architectures using Kafka for real-time AI applications and event‑driven AI workflows.
• Manage AI infrastructure including model serving on Open Shift/Kubernetes, monitoring, and performance optimization.
• Integration & Delivery
• Develop RESTful APIs and integration patterns for AI services consumption by internal and external applications.
• Collaborate with IT teams to integrate AI capabilities into existing banking systems and workflows.
• Participate in Agile delivery processes (Scrum, Kanban), contributing to sprint planning, code reviews, and continuous improvement.
• Quality & Standards
• Ensure AI solutions comply with the bank's security, governance, and regulatory requirements.
• Maintain code quality through testing, documentation, CI/CD practices, and adherence to engineering best practices.
• Stay current with emerging AI technologies, tools, and frameworks to continuously improve delivery capabilities.

• AI & ML Engineering
• Strong understanding of LLMs, embedding models, and generative AI architectures.
• Experience with AI orchestration frameworks, agent development, and multi‑step AI workflows.
• Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, Milvus) and RAG patterns.
• Knowledge of deep learning frameworks (Tensor Flow, PyTorch) and model deployment tools (MLflow, TFX).
• Software Engineering
• Python (Advanced level) and relevant AI/ML libraries (Lang Chain, Llama Index, or similar).
• Experience with API development (REST) and microservices architecture.
• SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries).
• Git (Git Hub/Git Lab) for version control and code management.
• Cloud & Data Engineering
• Experience with cloud platforms (Azure, GCP) and object storage (S3, GCS, ABS).
• Knowledge of data streaming technologies (Kafka) and workflow orchestration (Airflow, Apache NiFi).
• Containerization and…