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MultiBank Group in Dubai seeks a Senior Machine Learning Engineer to own ML products and infrastructure, shaping the ML lifecycle from design to deployment. This hands-on role drives scalable AI systems used in real-time environments, balancing performance, cost, and maintainability.
You will deploy and operate models across production pipelines, collaborate with product and data teams, and mentor engineers while advancing AI platform direction within a regulated, global fintech context.
MultiBank Group is a global financial pioneer established in 2005 in California and now headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.
We serve a thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.
We are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle - from model design through deployment, optimization, and ongoing performance in production - and contributes to the technical direction of the AI platform.
Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)
LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelines
MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks
Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures