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Niche LLM+full-stack Azure skillset and senior level reduces applicant competition.
ML/AI LLM specialization with regulated banking context reduces transferability moderately.
Many mandatory technical stacks, senior title, and production AI responsibilities imply strict filtering.
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Design, build, and operate AI-enabled applications and agentic workflows for a regulated banking environment, focusing on practical AI and automation solutions in production.
Develop full-stack backend services (Kotlin/Java, Spring Boot) and frontend interfaces (React, SolidJS, TypeScript) with secure API integrations and cloud delivery on Microsoft Azure.
Manage CI/CD pipelines, deploy and monitor production systems, ensuring reliability, performance, and iterative improvements based on real usage and business feedback.
Strong full-stack engineering experience with Kotlin/Java, Spring Boot, TypeScript, and frontend frameworks like React or similar.
Hands-on experience with AI engineering concepts including LLMs, prompting, retrieval-augmented generation (RAG), agents, embeddings, and AI workflow orchestration.
Working knowledge of Microsoft Azure cloud services, DevOps skills with CI/CD pipelines, deployment, monitoring, and troubleshooting in cloud environments.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in integrating AI agents with enterprise APIs, databases, and systems, preferably in regulated banking or fintech contexts.
Comfortable with hands-on roles combining AI engineering, software development, and cloud operations with strong ownership on delivery and iterative solution improvement.
Familiar with Microsoft Azure ecosystem including Azure AI Foundry, Entra ID, and enterprise tools like SharePoint, and modern orchestration/workflow frameworks (n8n, LangChain, etc.).