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our talents and on how we partner with our clients and communities to create lasting value.Job DescriptionIntroduction of the teamAt EFG we want to create value fom data. Within our Data Office department, were 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. Main responsibilities1) Platform and Architecture 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.2) Model Development & EvaluationCollaborate 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. 3) Software Development & MLOpsOwn 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.4) Product & Stakeholder CollaborationWork 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. 5) Security, Privacy, and ComplianceEnsure 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 EducationAdvanced degree in Computer Science, Data Science, Mathematics, Statistics, Physics, or related. Must-HaveExtensive knowledge of ML/AI frameworks: PyTorch or TensorFlow; Hugging Face ecosystem; LangChain/LlamaIndex 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 onprem/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 (GitHub Actions/GitLab CI/Azure DevOps). Data engineering proficiency: SQL, data modeling, ETL/ELT, and working with warehouses/lakes (Snowflake, BigQuery, S3/Delta).Ability to work in a SCRUM/Agile environment with a focus on delivery and stakeholder collaboration. Excellent analytical and problem-solving abilities; results- and detail-oriented with strong written and verbal communication.Experience in the Banking Or Wealth Management Industry is an advantage Nice-to-HaveExperience deploying and optimizing open-source models (Llama, Mistral, Mixtral) on GPUs; quantization (INT8/4), tensor/Flash attention. Knowledge of retrieval systems (BM25, hybrid search), semantic caching, and structured tool use/agents. Evaluation expertise for LLMs: rubric-based grading, golden sets, adversarial testing, and A/B experimentation. Security practices for AI applications: prompt injection defenses, output filtering, content moderation, and red-teaming. Contributions to open-source AI projects or published work.Our ValuesAccountability: Taking ownership for tasks and challenges, as well as seeking continuous improvementHands-on: Being proactive to rapidly deliver high-quality resultsPassionate: Being committed and striving for excellenceSolution-driven: Focusing on client outcomes and treating clients fairly with a risk-aware mindsetPartnership-oriented: Promoting collaboration and teamwork. Working together with an entrepreneurial spirit.Please ensure to attach a cover letter to your CV when filling the application.ApplicationShould you wish to apply for this position use this link to apply.
