Senior AI Engineer
Role Overview
The Senior AI Engineer leads design, development, and deployment of complex agentic AI solutions and enterprise AI platforms within the Data & AI Lab.
The role combines hands-on delivery and technical leadership to produce scalable, production-grade solutions aligned with banking regulatory requirements.
The position drives adoption of MCP, cloud-native AI services, and modern AI tools across the organization.
Key Skills for This Role
Full Job Posting
About Commercial Bank of Qatar
Founded in 1975 and headquartered in Doha, Commercial Bank offers personal, business, government, international, and investment services.
Job summary
The Senior AI Engineer leads design, development, and deployment of complex agentic AI solutions and enterprise AI platforms within the Data & AI Lab.
The role combines hands-on delivery and technical leadership to produce scalable, production-grade solutions aligned with banking regulatory requirements.
The position drives adoption of MCP, cloud-native AI services, and modern AI tools across the organization.
Key accountabilities
- Lead agentic AI solution architecture, autonomous workflows, multi-agent systems, and enterprise orchestration.
- Architect AI platform components, document processing frameworks, and bank-wide API and integration standards.
- Lead AI cloud architecture on Azure and GCP; design Kafka event-driven systems and establish Kubernetes/OpenShift model-serving and MLOps standards.
- Mentor engineers; lead design and code reviews; uphold CI/CD, testing, code management, and documentation practices.
- Partner with product owners and business stakeholders to translate requirements into technical solutions.
- Communicate technical trade-offs to senior management and steering committees.
- Contribute to AI governance and compliance with data policies, ethical standards, and regulatory requirements.
- Define AI solution SLAs and implement monitoring and alerting.
Technical competencies
- AI/ML: LLMs, embedding models, generative AI, orchestration frameworks, agent development, multi-agent systems, vector databases, RAG, TensorFlow, PyTorch, MLflow, and TFX.
- Software: advanced Python; LangChain, LlamaIndex, Semantic Kernel, or similar; API design, microservices, event-driven architecture, SQL, Git, and CI/CD.
- Cloud and data: Azure and/or GCP, object storage, Kafka, Spark, Airflow or Apache NiFi, Docker, Kubernetes/OpenShift, and MLOps.
- The supplied description ends during its Leadership & Communication competency section; later details are not available.
Experience and expertise
- Deep AI/ML engineering expertise, including LLMs, embedding models, generative AI, agent frameworks, vector databases, and RAG.
- Advanced Python, SQL, cloud, data engineering, containerization, and MLOps experience as detailed in the competencies.
- Financial-services industry experience is required by the job summary.
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