AI Engineer
Role Overview
Design, develop, and deploy agentic AI solutions and AI-enabled platforms within the Data & AI Lab.
Build AI infrastructure integrating cloud platforms, vector stores, document-processing pipelines, and real-time data streaming.
Work with AI Product Owners, Data Scientists, and engineers to deliver scalable production solutions for banking operations and customer-facing applications.
Key Skills for This Role
Full Job Posting
Role overview
Design, develop, and deploy agentic AI solutions and AI-enabled platforms within the Data & AI Lab.
Build AI infrastructure integrating cloud platforms, vector stores, document-processing pipelines, and real-time data streaming.
Work with AI Product Owners, Data Scientists, and engineers to deliver scalable production solutions for banking operations and customer-facing applications.
Key accountabilities
- Design autonomous workflows, multi-agent systems, AI orchestration patterns, and integrations with MCP or similar frameworks.
- Build AI pipelines, vector stores, embedding pipelines, retrieval systems, and document-processing and metadata-extraction pipelines for knowledge management and RAG.
- Develop APIs and integration layers; connect AI services with banking applications and existing systems.
- Deploy cloud AI solutions on Azure or GCP and implement Kafka streaming for real-time and event-driven use cases.
- Manage model serving on OpenShift/Kubernetes, monitoring, and performance optimization.
- Contribute to Agile delivery, sprint planning, code reviews, testing, documentation, CI/CD, and continuous improvement.
- Ensure AI solutions comply with security, governance, regulatory, and engineering standards.
Technical competencies
- Strong understanding of LLMs, embedding models, generative AI architectures, agent development, and multi-step workflows.
- Python at an advanced level; AI/ML libraries such as LangChain, LlamaIndex, or similar.
- Knowledge of REST APIs, microservices, advanced SQL, and Git/GitHub/GitLab.
- Experience with Azure or GCP and object storage such as S3, GCS, or ABS.
- Knowledge of Kafka, Airflow or Apache NiFi, Docker, Kubernetes/OpenShift, Spark, and MLOps.
- Familiarity with vector databases such as Azure AI Search, Pinecone, Weaviate, or Milvus; deep learning frameworks such as TensorFlow or PyTorch; and deployment tools such as MLflow or TFX.
- Effective cross-functional collaboration and communication with technical and non-technical stakeholders; Agile/Scrum or Kanban experience.
Qualifications and experience
- Bachelor’s or master’s degree in Computer Science, Software Engineering, Data Science, or a related field.
- 4–6 years of software engineering experience, including at least 2 years focused on AI/ML applications.
- Hands-on experience with Azure or GCP and containerization using Docker and OpenShift/Kubernetes.
- Document processing, metadata extraction, and knowledge management experience is preferred.
- Banking or financial services experience is a plus.
- Azure AI Engineer or GCP ML Engineer certifications are preferred.
Why Commercial Bank
- The employer highlights an innovation-driven, digital-first environment, global partnerships, employee well-being, work-life balance, and competitive compensation and benefits.
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