Senior AI Engineer

Role summary

AI architecture and platforms

• Lead autonomous workflows, multi-agent systems, enterprise AI orchestration, and MCP integration patterns.
• Architect vector stores, embedding pipelines, retrieval systems, and enterprise document processing and metadata extraction.
• Define API standards and integration patterns for AI services 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 and establish model-serving, monitoring, and MLOps standards on OpenShift/Kubernetes.
• Mentor AI Engineers; lead design and code reviews; support CI/CD, testing, documentation, and code management practices.

Stakeholder engagement and governance

• Translate business requirements into technical solutions with AI Product Owners and stakeholders; align with enterprise architecture and IT strategy.
• Communicate technical trade-offs to senior management and steering committees.
• Support AI governance and compliance with data policies, ethical standards, and regulatory requirements; define SLAs and monitoring.

AI and software expertise

• Deep expertise in LLMs, embedding models, generative AI, orchestration frameworks, complex agents, and multi-agent systems.
• Expert Python proficiency; experience with LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.
• Strong API design, microservices, event-driven architecture, and advanced SQL capabilities.

Platforms and tools

• Experience with Azure and/or GCP, object storage, Kafka, Spark, Airflow or Apache NiFi, Docker, and Kubernetes/OpenShift.
• Knowledge of vector databases, RAG, TensorFlow or PyTorch, MLflow or TFX, Git, CI/CD, and MLOps practices.

Industry and delivery

• Financial services experience and ability to address banking regulatory requirements.
• Experience managing end-to-end ML lifecycles, model deployment, monitoring, versioning, and management.