Senior AI Engineer

Role summary

AI architecture and platforms

• Lead agentic AI solutions, autonomous workflows, multi-agent systems, and enterprise AI orchestration.
• Define agent development patterns and Model Context Protocol integration practices.
• Architect vector stores, embedding pipelines, retrieval systems, and scalable document processing and metadata extraction.
• Define API standards and integration patterns for bank-wide AI services.
• Evaluate emerging AI technologies and recommend adoption strategies.

Cloud and technical leadership

• Lead AI cloud architecture on Azure and GCP with attention to scalability, security, and cost.
• Design Kafka event-driven architectures for real-time AI and streaming.
• Set model-serving, monitoring, and MLOps standards on OpenShift/Kubernetes.
• Mentor engineers; 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

• Translate business requirements into technical solutions with AI product owners and stakeholders.
• Communicate technical concepts and trade-offs to management and steering committees.
• Support AI governance and compliance with data policies, ethical standards, and regulatory requirements.
• Define SLAs and implement monitoring and alerting; align solutions with enterprise technology strategy and target data architecture.

AI and software competencies

• Expertise in LLMs, embedding models, generative AI, orchestration frameworks, agents, multi-agent systems, vector databases, RAG, and knowledge retrieval.
• Python and frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Deep learning frameworks include TensorFlow or PyTorch; deployment tools include MLflow or TFX.
• API design, microservices, event-driven architecture, SQL, Git, and CI/CD.

Cloud and data competencies

• Azure and/or GCP, with object storage such as S3, GCS, or ABS.
• Kafka, Spark, and workflow orchestration such as Airflow or Apache NiFi.
• Docker, Kubernetes/OpenShift, and MLOps including model monitoring, versioning, and management.

Experience and expertise

• The role calls for financial services industry experience and expertise in AI engineering, cloud architecture, and integration patterns.
• The source description ends during the Leadership & Communication competency section; remaining requirements are unavailable.

About Commercial Bank