Senior AI Engineer

About Commercial Bank Of QatarCommercial 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.Job SummaryThe Senior AI Engineer is responsible for leading the design, development, and deployment of complex Agentic AI solutions and enterprise AI platforms within the Data & AI Lab. This senior role requires deep expertise in AI engineering, cloud architecture, and integration patterns, combined with financial services industry experience.The position serves as a technical leader for AI initiatives, providing guidance on architecture decisions, best practices, and technology selection. The Senior AI Engineer will drive the adoption of AI orchestration frameworks (MCP), cloud-native AI services, and modern AI tooling across the organization.The role requires a balance of hands‑on technical delivery and technical leadership, ensuring that AI solutions are production‑grade, scalable, and aligned with banking regulatory requirements.Key AccountabilitiesAgentic AI Architecture & DevelopmentLead the design and implementation of complex Agentic AI solutions including autonomous workflows, multi-agent systems, and enterprise AI orchestration.Define architecture patterns and best practices for AI agent development and Model Context Protocol (MCP) integrations.Drive innovation in AI capabilities, evaluating emerging technologies and recommending adoption strategies.AI Platform LeadershipArchitect and oversee development of AI platform components including vector stores, embedding pipelines, and retrieval systems.Design scalable document parsing, processing, and metadata extraction frameworks for enterprise knowledge management.Define API standards and integration patterns for AI services consumption across the bank.Cloud Architecture & InfrastructureLead cloud architecture for AI solutions on Azure and GCP, ensuring scalability, security, and cost optimization.Design event‑driven architectures using Kafka for real‑time AI applications and streaming cases.Establish infrastructure standards for model serving on OpenShift/Kubernetes, monitoring, and MLOps practices.Technical LeadershipProvide technical mentorship to AI Engineers and contribute to team capability building.Lead technical design reviews, code reviews, and architecture discussions.Ensure adherence to best practices: CI/CD, code management, testing, knowledge management, and documentation.Collaborate with enterprise architecture and IT teams to ensure AI solutions align with bank‑wide technology strategy and target data architecture.Stakeholder EngagementPartner with AI Product Owners and business stakeholders to translate requirements into technical solutions.Communicate technical concepts and trade‑offs to senior management and steering committees.Contribute to AI governance, ensuring solutions comply with data policies, ethical standards, and regulatory requirements.Define and maintain SLAs for AI solutions, implementing necessary monitoring and alerting.Key CompetenciesAI & ML EngineeringDeep expertise in LLMs, embedding models, and generative AI architectures.Advanced experience with AI orchestration frameworks, complex agent development, and multi‑agent systems.Expert knowledge of vector databases, RAG architectures, and knowledge retrieval patterns.Strong background in deep learning frameworks (TensorFlow, PyTorch) and model deployment tools (MLflow, TFX).End‑to‑end ML lifecycle management and model monitoring.Software EngineeringExpert proficiency in Python (Advanced level) and AI/ML frameworks (LangChain, LlamaIndex, Semantic Kernel, or similar).Strong experience with API design, microservices architecture, and event‑driven architecture.SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries).Git (GitHub/GitLab), CI/CD pipelines, and code management best practices.Cloud & Data EngineeringExpert‑level experience with cloud platforms (Azure and/or GCP) and object storage (S3, GCS, ABS).Advanced knowledge of Kafka, Spark, and workflow orchestration (Airflow, Apache NiFi).Expert in containerization and orchestration: Docker, K8s (OpenShift).MLOps practices including model monitoring, versioning, and management.Leadership & CommunicationProven ability to lead technical initiatives and mentor junior engineers.Strong stakeholder management and communication skills.Data visualization and reporting for executive communication.Experience collaborating across organizational boundaries with IT, security, and compliance teams.Qualifications & ExperienceBachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or related field.7+ years of experience in software engineering with at least 4 years focused on AI/ML applications.Demonstrated experience leading technical delivery of AI solutions in production environments.Expert‑level experience with cloud platforms (Azure