Senior AI Engineer

About Commercial Bank of Qatar

Job Summary

Key Accountabilities

• Lead agentic AI solutions, autonomous workflows, multi-agent systems, and enterprise AI orchestration; define development patterns and MCP integration practices.
• Evaluate emerging AI technologies and recommend adoption strategies.
• Architect vector stores, embedding pipelines, retrieval systems, and scalable document parsing, processing, and metadata extraction frameworks.
• Define API standards and integration patterns for bank-wide AI service consumption.
• Lead AI cloud architecture on Azure and GCP, ensuring scalability, security, and cost optimization.
• Design Kafka event-driven architectures for real-time AI and streaming use cases.
• Set infrastructure standards for model serving on OpenShift/Kubernetes, monitoring, and MLOps.
• Mentor AI Engineers and lead technical design reviews, code reviews, and architecture discussions.
• Ensure practices for CI/CD, code management, testing, knowledge management, and documentation.
• Collaborate with enterprise architecture and IT to align AI solutions with bank technology strategy and target data architecture.
• Translate requirements with AI Product Owners and business stakeholders; explain technical concepts and trade-offs to senior management and steering committees.
• Contribute to AI governance and regulatory compliance; define AI solution SLAs and implement monitoring and alerting.

AI and ML Engineering

• Deep 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 TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Experience with end-to-end ML lifecycle management and model monitoring.

Software Engineering

• Expert advanced Python proficiency and AI/ML frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Strong experience with API design, microservices, and event-driven architecture.
• Advanced SQL skills, including stored procedures, window functions, temporary tables, and recursive queries.
• Experience with GitHub or GitLab, CI/CD pipelines, and code management practices.

Cloud and Data Engineering

• Expert-level experience with Azure and/or GCP and object storage such as S3, GCS, or ABS.
• Advanced knowledge of Kafka, Spark, and workflow orchestration such as Airflow or Apache NiFi.
• Expertise in Docker, Kubernetes, or OpenShift.
• MLOps experience with model monitoring, versioning, and management.