Senior AI Engineer

Role overview

AI architecture and platforms

• Lead agentic AI, autonomous workflow, multi-agent system, and enterprise orchestration development.
• Define AI agent architecture patterns and Model Context Protocol integration practices.
• Architect vector stores, embedding pipelines, and retrieval systems.
• Design document parsing, processing, and metadata extraction for enterprise knowledge management.
• Define API standards and AI service integration patterns across the bank.

Cloud and technical leadership

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

Stakeholder engagement and governance

• Collaborate with enterprise architecture, IT, product owners, and business stakeholders to develop aligned technical solutions.
• Explain technical concepts and trade-offs to senior management and steering committees.
• Contribute to AI governance and compliance with data policies, ethical standards, and regulations.
• Define AI solution SLAs and implement monitoring and alerting.

Technical competencies

• Deep expertise in LLMs, embedding models, generative AI architectures, agent development, and multi-agent systems.
• Expertise in vector databases, RAG, knowledge retrieval, and AI orchestration frameworks.
• Python, TensorFlow or PyTorch, MLflow or TFX, LangChain, LlamaIndex, Semantic Kernel, APIs, microservices, SQL, and Git.
• Azure or GCP, cloud object storage, Kafka, Spark, Airflow or Apache NiFi, Docker, Kubernetes, and OpenShift.
• MLOps, model lifecycle management, monitoring, versioning, and deployment.

Qualifications and experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field.
• 7+ years of software engineering experience, including at least 4 years focused on AI/ML applications.
• Demonstrated leadership delivering AI solutions in production environments.
• Expert-level Azure or GCP, OpenShift/Kubernetes, and enterprise integration experience.
• Banking or financial services experience is mandatory.
• Financial-services regulatory compliance, data governance, and security experience are preferred.
• Azure AI Engineer, GCP ML Engineer, or equivalent certifications are strongly preferred.

About Commercial Bank