Senior AI Engineer with Azure OpenAI, Banking AI Platform

Ларнака

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Project overview

The project focuses on evolving a modern AI platform within a banking environment, embedding generative AI into customer facing journeys and internal engineering workflows. It includes building AI agents and copilots, implementing retrieval augmented generation over enterprise data, and establishing shared AI patterns and governance.

Team

You will work in a cross functional team consisting of AI engineers, software engineers, platform specialists, data professionals, and product stakeholders. The team collaborates closely across domains, following cloud native and DevOps oriented delivery practices, with a strong focus on security, reliability, and responsible AI.

Position overview

We are looking for a Senior AI Engineer to design, build, and operate AI powered solutions that enhance digital banking and internal engineering platforms. You will work closely with product, development, data, and platform teams to translate business needs into secure and scalable AI use cases, from early ideation and prototyping through production deployment and lifecycle management. The role combines hands on engineering with platform thinking, enabling enterprise wide adoption of generative AI in a regulated environment.

Technology stack

Microsoft Azure, Azure OpenAI, Azure AI Services for Vision Speech Language and Document Intelligence, Azure AI Search, Azure Cosmos DB, PostgreSQL with pgvector, Pinecone Weaviate or Qdrant, Python, C#, JavaScript, TypeScript, Azure App Service, Kubernetes, Azure DevOps, GitHub Actions, Azure API Management, Kong, Apigee, OAuth2, OIDC, Azure Entra ID, Azure Key Vault

Responsibilities

Design, build, and operate AI powered solutions and agents for digital channels and internal engineering platforms
Develop custom AI agents and copilots and integrate them into web applications mobile applications and internal tools
Implement retrieval augmented generation solutions over enterprise data using vector search technologies
Collaborate with product and engineering teams to translate business requirements into scalable AI solutions
Deploy and operate AI workloads in cloud native environments using managed services and Kubernetes
Integrate AI capabilities through secure and governed APIs using API gateway platforms
Implement observability evaluation and monitoring for AI solutions across their lifecycle
Apply responsible AI principles including safety controls prompt filtering and compliance requirements
Contribute to shared AI platform patterns and enable other teams to adopt generative AI at scale
Support authentication authorization and secrets management for AI enabled services
Continuously improve AI solutions through testing feedback loops and iterative enhancements

Requirements

Strong hands on experience designing and implementing AI solutions on a major public cloud platform
Experience with managed AI and large language model services such as Azure OpenAI and Azure AI Services
Practical experience building and deploying AI agents or copilots using platforms such as Azure AI Studio Foundry or Copilot Studio
Solid understanding of retrieval augmented generation architectures and vector search
Hands on experience with vector search services and vector capable data stores
Proficiency in at least one programming language used for AI development such as Python C# or JavaScript TypeScript
Experience working with AI and LLM SDKs and REST APIs
Experience deploying AI workloads into cloud native applications using platforms such as Azure App Service or Kubernetes
Experience working with CI CD pipelines and DevOps tooling
Familiarity with API gateway platforms for securing routing and governing APIs
Experience with authentication and authorization mechanisms including OAuth2 OIDC and managed identities
Understanding of responsible AI principles privacy considerations and regulatory constraints
Experience working in enterprise or regulated environments

Nice to have

Experience with AI services from other cloud providers such as Amazon Bedrock or Google Vertex AI
Exposure to MCP or similar connector frameworks for integrating LLM agents with internal and external systems
Experience with MLOps practices including monitoring automated testing staged rollouts and feedback driven improvements
Experience contributing to shared AI platforms or internal developer enablement initiatives

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