AI Engineer

General Info

• Department:
  Data Office
• Worktime Percentage: 100%
• Location:
  
  Geneva

EFG International is a global private banking group, offering private banking and asset management services. We serve clients in over 40 locations worldwide. EFG International offers a stimulating and dynamic work environment and strives to be an employer of choice.

EFG is committed to providing an equitable and inclusive working environment that is founded on the principle of mutual respect. Joining our team means experiencing a supportive environment, where your contributions are valued and recognised. We strongly believe that the diversity of our teams gives us a competitive advantage by fostering better decision-making and greater innovation.

Empowering entrepreneurial minds to create value – today and for the future.

We are a private bank, offering personalised solutions on a global scale to private and institutional clients. Our sustainable success is based on our talents and on how we partner with our clients and communities to create lasting value.

Introduction of the team

• At EFG we want to create value fom data. Within our Data Office department, we’re seeking a highly skilled and motivated AI Software Engineer to join our Machine Learning & GenAI team. You will embark on building a new, scalable AI platform and design, build, and deploy AI-driven systems that deliver measurable business impact.
• This is an excellent opportunity to make a significant impact in a growing organization committed to delivering an outstanding digital banking experience for our clients.

• Design and build a hybrid (on-prem / on-cloud) AI/ML platform to run AI use cases at scale (feature stores, model registry, experimentation, evaluation, observability).
• Define and implement secure, reliable inference and training architectures, including vector search and RAG components where applicable.
• Provide platform support for embeddings, vector databases, and AI agentic communication protocols to enable grounded, interoperable AI workflows.
• Document machine learning processes, system architecture, and operational runbooks for reproducibility and knowledge sharing.

• Collaborate on training, fine-tuning, and optimizing models (LLMs, NLP, recommendations), including LoRA/PEFT when relevant.
• Implement guardrails and prompt strategies to reduce hallucinations and improve safety and consistency, and support agentic workflows.
• Establish evaluation frameworks for RAG and LLM systems.

• Own end-to-end software development of AI services and APIs (from design and coding to testing and deployment).
• Automate build, test, and deployment using CI/CD pipelines; manage model/version releases via model registries.
• Implement continuous monitoring for deployed AI systems.

• Work closely with Business Users, Product Owners, Business Engineers, Data Managers, Data Scientists, and technology teams to understand AI/ML use cases, requirements, and success metrics.
• Partner with Data Scientists to iterate on prototypes and convert them into robust, scalable production services.
• Translate emerging GenAI/Agentic AI capabilities into actionable product opportunities and reusable components for the Bank.

• Ensure all AI/ML solutions meet bank-wide data and AI guidelines and standards, including data protection, cyber security, and responsible AI practices.
• Embed privacy-by-design, access controls, encryption, and auditability across data flows and model operations.
• Collaborate with Risk, Security, and Compliance to align with SOC 2, GDPR/CCPA, and internal governance.

Skills and experience

1) Education

• Advanced degree in Computer Science, Data Science, Mathematics, Statistics, Physics, or related.

2) Must-Have

• Extensive knowledge of ML/AI frameworks:
  PyTorch or Tensor Flow;
  Hugging Face ecosystem;
  Lang Chain/Llama Index or equivalent for orchestration, data structures, data modeling, and software architecture.
• Practical LLM experience: prompt engineering, fine-tuning/LoRA, embeddings, vector databases (FAISS, Pinecone, Weaviate), RAG patterns.
• Solid programming skills in Python, R or Java/Scala, hands on experience in SQL, ETL tool and Linux and Control-M & Terraform knowledge are a plus.
• Prior experience deploying applications on cloud environments (Azure); familiarity with hybrid on‑prem/cloud setups.
• Experience building production-grade services and APIs (REST/gRPC), cloud-native (AWS/GCP/Azure), containers (Docker), and orchestration (Openshift, Kubernetes).
• MLOps foundations: experiment tracking (MLflow/W&B), model registries, CI/CD, model monitoring, feature stores.
• Ability to monitor, debug, and maintain CI/CD pipelines that feed into production deployments (Git Hub Actions/Git Lab CI/Azure Dev Ops).
• Data engineering proficiency: SQL, data modeling, ETL/ELT, and working with…