Senior AI Engineer

Role summary

Agentic AI and platform engineering

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

Cloud and technical leadership

• Lead AI cloud architecture on Azure and GCP, with attention to scalability, security, and cost optimization.
• Design Kafka-based 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.
• Ensure sound CI/CD, code management, testing, knowledge management, and documentation practices.
• Collaborate with enterprise architecture and IT teams to align AI solutions with bank strategy and target data architecture.

Stakeholder engagement and governance

• Partner with AI Product Owners and business stakeholders to translate requirements into technical solutions.
• Explain technical concepts and trade-offs to senior management and steering committees.
• Contribute to AI governance and ensure compliance with data policies, ethical standards, and regulatory requirements.
• Define and maintain service-level agreements, monitoring, and alerting for AI solutions.

AI and software competencies

• Deep expertise in LLMs, embedding models, and generative AI architectures.
• Advanced experience with AI orchestration, 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 deployment tools such as MLflow or TFX.
• Expert proficiency in advanced Python and AI/ML frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Experience with API design, microservices, and event-driven architecture.
• Advanced SQL, including stored procedures, window functions, temporary tables, and recursive queries.

Cloud, data, and MLOps competencies

• 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 such as Airflow or Apache NiFi.
• Expertise in Docker, Kubernetes/OpenShift, and MLOps, including model monitoring, versioning, and management.
• The source excerpt ends during the competency section; subsequent listed qualifications are unavailable.

Role context

• The role requires financial services industry experience and alignment with banking regulatory requirements.

About the company