Senior AI Engineer
Role Overview
The Senior AI Engineer leads design, development, and deployment of complex agentic AI solutions and enterprise AI platforms within the Data & AI Lab.
The role combines hands-on delivery with technical leadership and requires production-grade, scalable solutions aligned with banking regulatory requirements.
The engineer guides architecture decisions, best practices, and technology selection for AI initiatives.
Key Skills for This Role
Full Job Posting
Role overview
The Senior AI Engineer leads design, development, and deployment of complex agentic AI solutions and enterprise AI platforms within the Data & AI Lab.
The role combines hands-on delivery with technical leadership and requires production-grade, scalable solutions aligned with banking regulatory requirements.
The engineer guides architecture decisions, best practices, and technology selection for AI initiatives.
Agentic AI and platforms
- Lead development of autonomous workflows, multi-agent systems, and enterprise AI orchestration.
- Define patterns for AI agent development and MCP integrations; evaluate emerging technology and recommend adoption.
- Architect vector stores, embedding pipelines, retrieval systems, and scalable document processing and metadata extraction.
- Set API standards and integration patterns for AI services across the bank.
Cloud and technical leadership
- Lead AI cloud architecture on Azure and GCP for scalability, security, and cost optimization.
- Design Kafka event-driven architectures for real-time AI and streaming.
- Set standards for model serving on OpenShift/Kubernetes, monitoring, and MLOps.
- Mentor AI Engineers; lead technical, code, and architecture reviews; uphold CI/CD, testing, code management, and documentation practices.
Stakeholders and governance
- Partner with product owners and business stakeholders to turn requirements into technical solutions.
- Communicate technical concepts and trade-offs to senior management and steering committees.
- Support AI governance and compliance with data policies, ethical standards, and regulatory requirements.
- Define and maintain AI solution SLAs and implement monitoring and alerting.
- Align solutions with enterprise architecture, IT teams, bank technology strategy, and target data architecture.
AI and software expertise
- Deep expertise in AI engineering, cloud architecture, integration patterns, LLMs, embedding models, and generative AI.
- 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.
- Expert proficiency in advanced Python and AI/ML frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Strong experience with API design, microservices, event-driven architecture, and advanced SQL.
Cloud, data, and MLOps 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.
- Expertise in Docker, Kubernetes/OpenShift, and MLOps, including model monitoring, versioning, and management.
- Financial services industry experience.
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