Senior AI Engineer

Role summary

Agentic AI and platform leadership

• Lead autonomous workflows, multi-agent systems, and enterprise AI orchestration; define agent development patterns and MCP integrations.
• Architect vector stores, embedding pipelines, retrieval systems, document processing, and metadata extraction for enterprise knowledge management.
• Define API standards and integration patterns for AI services across the bank.
• Evaluate emerging AI technologies and recommend adoption strategies.

Cloud and technical leadership

• Lead AI cloud architecture on Azure and GCP, considering scalability, security, and cost optimization.
• Design Kafka-based event-driven architecture for real-time AI applications and streaming use cases.
• Establish model-serving, monitoring, and MLOps infrastructure standards using OpenShift/Kubernetes.
• Mentor AI engineers; lead design reviews, code reviews, and architecture discussions.
• Promote CI/CD, code management, testing, knowledge management, and documentation practices.

Stakeholder engagement and governance

• Translate stakeholder requirements into technical solutions and communicate technical concepts and trade-offs to senior management and steering committees.
• Collaborate with enterprise architecture and IT to align solutions with bank technology strategy and target data architecture.
• 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 software engineering expertise

• Deep expertise in LLMs, embedding models, and generative AI architectures; advanced experience in orchestration and multi-agent development.
• Expert knowledge of vector databases, RAG, and knowledge retrieval patterns.
• Strong background in TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Expert Python proficiency and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Strong API design, microservices, and event-driven architecture experience; advanced SQL skills.

Cloud and infrastructure expertise

• 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.
• Expert knowledge of Docker and Kubernetes/OpenShift, with MLOps experience in model monitoring, versioning, and management.

Role context

• The role requires financial services experience and technical leadership, including guidance on architecture decisions, best practices, and technology selection.

About Commercial Bank