AI Platform Engineer Capabilities

Job summary

The AI Platform Engineer turns lessons from real AI implementations into reusable platform services, SDKs, templates, architectural blueprints, and governance patterns for use across a bank. The role defines AI architecture standards, builds shared capabilities for machine learning and agentic AI, improves developer experience, and delivers cloud-native automation and integrations. The platform uses Azure, Python, AKS and Kubernetes, Databricks, and CI/CD pipelines, with emphasis on security, compliance, reliability, and scalability.

Benefits

Qualifications

• Strong Python software engineering background, including design patterns, coding practices, packaging, and publishing.
• Azure systems engineering experience, including networking and Entra ID identity and access management.
• Experience with DevOps practices, version control, CI/CD, automated testing, and deployment strategies.
• Knowledge of Kubernetes reliability engineering and Helm.
• Understanding of the machine learning lifecycle, including training, tracking, and serving.
• Knowledge of MLOps, LLMOps, generative AI, AI agents, and retrieval-augmented generation.
• Knowledge of AI evaluation, such as Arize Phoenix or MLflow.
• Experience with observability and monitoring tools such as Grafana, Prometheus, and Azure Monitor.
• Security and governance awareness.
• Developer experience mindset, systems thinking, and architecture modeling.

Responsibilities

• Define and maintain AI architecture standards and reference architectures.
• Translate emerging AI implementation patterns into enterprise standards.
• Align regulatory requirements with architecture, security, risk, data governance, and platform teams.
• Design and develop shared AI services and platform capabilities.
• Build SDKs, accelerators, templates, and reference implementations for machine learning, agentic AI, and AI systems.
• Shape and maintain the bank's AI engineering developer experience.
• Reduce friction for teams building AI applications.
• Drive adoption of Machine Learning and Artificial Intelligence as a Platform capabilities.
• Design scalable, cloud-native solutions.
• Implement CI/CD pipelines and platform automation.
• Integrate AI services with AKS and supporting platforms.

Skills

Industry

Relocation