Senior AI Engineer
Skills
About This Role
Overview
The Senior AI Engineer is responsible for designing, developing, and deploying Agentic AI solutions and AI-enabled platforms within the Data & AI Lab.
The role focuses on building robust AI infrastructure, including integrations with cloud platforms, vector stores, document processing pipelines, and real-time data streaming capabilities.
This position requires strong engineering skills to work with AI orchestration frameworks (MCP), cloud services (Azure, GCP), and modern AI tooling.
The AI Engineer collaborates closely with AI Product Owners, Data Scientists, and other engineers to deliver production-grade AI solutions supporting banking operations and customer-facing applications.
The role bridges AI research and production deployment, ensuring AI capabilities are scalable, maintainable, and aligned with enterprise architecture standards.
1. Agentic AI Development
- Design and implement Agentic AI solutions, including autonomous workflows, multi-agent systems, and AI orchestration patterns.
- Develop and maintain integrations with Model Context Protocol (MCP) and similar AI orchestration frameworks.
- Build and optimize AI pipelines that combine multiple AI capabilities into cohesive solutions.
2. AI Platform Engineering
- Develop and maintain AI platform components, including vector stores, embedding pipelines, and retrieval systems.
- Implement document parsing, processing, and metadata extraction pipelines for knowledge management and RAG applications.
- Build and maintain APIs and integration layers for AI services consumption.
3. Cloud & Infrastructure
- Design and deploy AI solutions on cloud platforms (Azure, GCP) following best practices for scalability and security.
- Implement data streaming architectures using Kafka for real-time AI applications and event-driven AI workflows.
- Manage AI infrastructure, including model serving on OpenShift/Kubernetes, monitoring, and performance optimization.
4. Integration & Delivery
- Develop RESTful APIs and integration patterns for AI services consumption by internal and external applications.
- Collaborate with IT teams to integrate AI capabilities into existing systems and workflows.
- Participate in Agile delivery processes (Scrum, Kanban), contributing to sprint planning, code reviews, and continuous improvement.
5. Quality & Standards
- Ensure AI solutions comply with security, governance, and regulatory requirements.
- Maintain code quality through testing, documentation, CI/CD practices, and adherence to engineering best practices.
- Stay current with emerging AI technologies, tools, and frameworks to continuously improve delivery capabilities.
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