Project description

This position is attached to the production team and is responsible for defining cloud architecture direction, providing technical guidance, and ensuring that secure, scalable, resilient, and operable cloud solutions are adopted on the Group private cloud platform. The role works in close collaboration with application teams and Group service providers delivering PaaS and IaaS services, translating architecture principles into production-ready cloud patterns, deployment standards, automation guidelines, operational guardrails, and review practices, without being the primary implementer of the solutions.

Responsibilities

Design, develop and deploy Generative AI solutions (AI assistants, code generation tools, intelligent automation) that increase APS productivity

Collaborate with IT teams across the domains to identify high-impact use cases where AI can streamline development, testing, documentation or operations

Build AI agent architectures tailored to IT workflows, including agent orchestration and inter-agent communication protocols (e.g. Model Context Protocol – MCP)

Develop full-stack components supporting AI solutions: backend services (Python – mandatory), frontend interfaces, and API layers

Collaborate and Leverage the Group AI offering solutions and services (eg,: LLMaaS, RAGaaS, Data tools, etc.), APIs and components to build solutions efficiently while providing feedback for platform improvements

Implement and maintain CI/CD pipelines for AI solution deployment, ensuring quality, reproducibility and traceability

Design and manage containerized environments (Kubernetes/Docker) for AI workloads in the bank's private cloud infrastructure

Ensure all solutions comply with the bank's security, data privacy and governance standards, particularly around sensitive data handling and regulatory constraints

Conduct proof-of-concepts (PoCs) and prototyping to evaluate emerging AI tools and frameworks for potential adoption in IT use cases

Proactively identify opportunities for standardization, process improvement, deployment optimization, resilience enhancement, and innovation across cloud AI operations.

Skills

Must have

MUST be with computer science academic background

Must have 5+ years of experience as a AI Engineer by using Python

Full-stack development with strong Python expertise and API design capabilities

Hands-on experience with GenAI frameworks and tools (LangChain, LlamaIndex, Semantic Kernel, OpenAI API, Azure OpenAI or equivalent)

Understanding of AI agent orchestration and communication protocols (MCP, function calling, tool use)

Knowledge of data modelling, vector databases and RAG architectures

Containerization and orchestration (Docker, Kubernetes) in enterprise environments

CI/CD pipeline design and MLOps practices

Familiarity with cloud platforms (Azure preferred) and private cloud banking infrastructure

Understanding of IT workflows, software development lifecycle and DevOps practices — ability to identify where AI adds the most value

Proven track record of adapting open-source Large Language Models (LLMs) via techniques like LoRA, QLoRA, and full parameter fine-tuning is a plus

Advanced knowledge of Hugging Face libraries (transformers, peft, accelerate, datasets) for model training, evaluation, and deployment is a plus

Nice to have

Genuine passion and intellectual curiosity for AI, Generative AI and emerging technologies

Strong problem-solving and analytical skills with a solution-oriented mindset

Clear and effective communication skills, able to articulate complex technical concepts to non-technical stakeholders

Ability to collaborate and work effectively in a distributed, multi-cultural team environment

Attention to detail and rigor, particularly around code quality, testing and documentation

Proactive and autonomous, able to drive initiatives with minimal supervision while respecting governance frameworks

Other

Languages

English: B2 Upper Intermediate

Seniority

Regular

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