Senior AI Engineer

Role summary

Agentic AI and platform architecture

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

Cloud, infrastructure, and technical leadership

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

Stakeholder engagement and governance

• Partner with AI Product Owners and business stakeholders to translate requirements into technical solutions.
• 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, data policy, ethical, and regulatory compliance.
• Define and maintain AI solution SLAs and implement monitoring and alerting.

AI and software expertise

• The description calls for deep expertise in LLMs, embedding models, generative AI architectures, orchestration frameworks, 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.
• Advanced Python proficiency and experience with LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.
• Experience with API design, microservices, event-driven architecture, SQL, Git, and CI/CD.

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 and Kubernetes/OpenShift.
• MLOps practices include model monitoring, versioning, and management.
• The role also calls for financial services industry experience.

Requirements

• Deep expertise in AI engineering, cloud architecture, and integration patterns, with financial services industry experience.
• Advanced proficiency in Python and expertise in LLMs, embedding models, generative AI architectures, orchestration frameworks, and complex agent development.
• Expert knowledge of vector databases, RAG architectures, and knowledge retrieval patterns.
• Strong background in deep learning frameworks such as TensorFlow or PyTorch and model deployment tools such as MLflow or TFX.
• Strong experience with API design, microservices, event-driven architecture, Git, and CI/CD pipelines.
• 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 containerization and orchestration using Docker and Kubernetes/OpenShift.
• Experience with end-to-end ML lifecycle management, model monitoring, versioning, and MLOps.