Senior AI Engineer

About Commercial Bank of Qatar

Job Summary

Agentic AI and AI Platform

• Lead autonomous workflows, multi-agent systems, and enterprise AI orchestration.
• Define architecture patterns and best practices for AI agents and Model Context Protocol integrations.
• Architect vector stores, embedding pipelines, and retrieval systems.
• Design document parsing, processing, and metadata extraction frameworks for enterprise knowledge management.
• Define API standards and integration patterns for bank-wide AI services.

Cloud and Infrastructure

• Lead Azure and GCP cloud architecture for AI solutions, addressing scalability, security, and cost optimization.
• Design Kafka event-driven architectures for real-time AI applications and streaming use cases.
• Establish model-serving standards for OpenShift or Kubernetes, monitoring, and MLOps.

Technical Leadership and Stakeholders

• Mentor AI engineers and support team capability building.
• Lead technical design reviews, code reviews, and architecture discussions.
• Promote CI/CD, code management, testing, knowledge management, and documentation practices.
• Collaborate with enterprise architecture and IT teams to align AI solutions with bank-wide technology and data architecture.
• Translate stakeholder requirements into technical solutions and communicate trade-offs to senior management and steering committees.
• Contribute to AI governance and compliance with data policies, ethical standards, and regulatory requirements.
• Define and maintain AI solution SLAs and implement monitoring and alerting.

AI and Machine Learning Competencies

• Deep expertise in LLMs, embedding models, generative AI architectures, AI orchestration, agent development, and multi-agent systems.
• Expert knowledge of vector databases, RAG architectures, and knowledge retrieval patterns.
• Strong background in TensorFlow or PyTorch and deployment tools such as MLflow or TFX.
• Experience with end-to-end ML lifecycle management and model monitoring.

Software, Cloud, and Data Competencies

• Advanced Python and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Experience with API design, microservices, and event-driven architecture; advanced SQL skills are listed.
• Expert-level Azure and/or GCP experience and object storage such as S3, GCS, or ABS.
• Advanced Kafka, Spark, and workflow orchestration knowledge, including Airflow or Apache NiFi.
• Expertise in Docker and Kubernetes, including OpenShift, and MLOps practices for model monitoring, versioning, and management.

Requirements

• Deep expertise in AI engineering, cloud architecture, integration patterns, and financial services experience.
• Deep expertise in LLMs, embedding models, generative AI architectures, orchestration frameworks, 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.
• Advanced Python proficiency and expertise in AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Strong experience with API design, microservices, and event-driven architecture.
• 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 tools such as Airflow or Apache NiFi.
• Expertise in Docker and Kubernetes, including OpenShift.
• Experience with MLOps, model monitoring, versioning, and management.