AI Engineer

About Commercial Bank of Qatar

Since its inception in 1975, Commercial Bank of Qatar, based in Doha, has significantly contributed to Qatar's economic progress by providing a variety of personal, business, governmental, international, and investment banking services. The bank is committed to empowering its workforce, encouraging professional development, and fostering a workplace culture that embraces innovation, work-life balance, and employee potential.

Role Overview

The AI Engineer will be responsible for designing, developing, and deploying advanced Agentic AI systems and platforms within the Data & AI Lab. This entails building a resilient AI infrastructure with integrations across cloud platforms, vector storage solutions, document processing workflows, and real-time streaming data capabilities.

This role requires extensive engineering expertise with AI orchestration frameworks such as MCP, various cloud services including Azure and GCP, and modern AI toolkits. Close collaboration with AI Product Owners, Data Scientists, and engineering teams is essential to deliver AI applications that enhance banking operations and customer experiences.

The engineer bridges the gap between AI research and enterprise deployment, ensuring scalable, maintainable AI solutions in line with architectural standards.

Key Responsibilities

• Design and implement autonomous Agentic AI workflows, multi-agent systems, and orchestration patterns.
• Develop and maintain integrations with AI orchestration frameworks such as Model Context Protocol (MCP).
• Create and optimize AI pipelines combining multiple AI functionalities into unified solutions.
• Engineer AI platform components including vector databases, embeddings, and retrieval mechanisms.
• Implement document parsing, processing, and metadata extraction pipelines to support knowledge management and retrieval augmented generation (RAG) use cases.
• Build and sustain APIs and integration layers to facilitate AI service consumption across banking applications.
• Deploy AI solutions on cloud platforms (Azure and GCP), ensuring scalability and security best practices.
• Design data streaming architectures with Kafka for real-time AI applications and event-driven workflows.
• Manage AI infrastructure, including model serving on OpenShift/Kubernetes environments, monitoring, and performance tuning.
• Develop RESTful APIs and integration strategies for internal and external consumption of AI services.
• Coordinate with IT teams to embed AI capabilities into existing banking systems and workflows.
• Engage in Agile software delivery practices such as Scrum and Kanban, contributing to planning, code reviews, and continuous improvement.
• Ensure compliance with security, governance, and regulatory policies for all AI solutions.
• Maintain high code quality through rigorous testing, documentation, continuous integration/deployment, and engineering best practices.
• Stay informed on the latest AI technologies, tools, and frameworks to enhance solution delivery.

Required Competencies

• In-depth knowledge of large language models (LLMs), embedding mechanisms, and generative AI structures.
• Experience with AI orchestration frameworks, agent development, and multi-step AI process workflows.
• Familiarity with vector databases such as Azure AI Search, Pinecone, Weaviate, and Milvus, and understanding of retrieval augmented generation (RAG) patterns.
• Proficiency with deep learning frameworks like TensorFlow and PyTorch, alongside model deployment tools including MLflow and TFX.
• Advanced skills in Python programming and AI/ML libraries such as LangChain or LlamaIndex.
• Experience developing RESTful APIs and working with microservices architecture.
• Strong SQL skills, including stored procedures, window functions, temporary tables, and recursive queries.
• Version control expertise using Git platforms like GitHub or GitLab.
• Hands-on experience with cloud platforms (Azure, GCP), and knowledge of object storage systems (S3, GCS, ABS).
• Familiarity with data streaming technologies (Kafka) and workflow orchestration tools like Airflow or Apache NiFi.
• Containerization and orchestration skills using Docker and Kubernetes (OpenShift).
• Understanding of Spark for data processing and MLOps methodologies.
• Ability to collaborate effectively with cross-functional teams including Data Scientists, ML Engineers, and Product Owners.
• Strong communication skills to convey technical concepts to non-technical audiences.
• Experience working within Agile methodologies, including Scrum and Kanban.

Qualification and Experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or related discipline.
• Between 4 to 6 years of software engineering experience, with a minimum of 2 years focusing on AI and machine learning applications.
• Practical experience using cloud platforms (Azure or GCP) and containerization technologies (Docker, OpenShift/Kubernetes).
• Preferred experience in document processing, metadata extraction, and knowledge management systems.
• Experience in the banking or financial industry is considered advantageous.
• Relevant certifications such as Azure AI Engineer or GCP ML Engineer are beneficial.

Why Work with Commercial Bank?

• Awarded Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance.
• An innovation-centric and digital-first environment using cutting-edge tools and technologies to transform banking.
• Access to global partnerships and international exposure, promoting broad networking and perspectives.
• Emphasis on employee well-being and work-life balance in a supportive workplace.
• Competitive salary and benefits rewarding dedication and performance.
• A robust commitment to diversity, equity, and inclusion with a culture valuing unique viewpoints.

Disclaimer

Only shortlisted candidates will be contacted for further recruitment steps. The company reserves the right to amend, withdraw, or close this job posting at any time without notice.