Senior Machine Learning Engineer

About Us

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.

The Role

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.

Responsibilities

• Architect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day one
• Build and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scale
• Own technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainability
• Design and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applications
• Implement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines
• Continuously optimize model performance, inference latency, cost efficiency, and reliability across live systems
• Collaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutions
• Mentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the team
• Contribute to strategic decisions around data architecture, AI infrastructure, and cloud platform direction
• Work with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimization

Requirements

• 7 to 15 or more years of experience in software engineering, data science, or ML engineering
• Strong background in product companies, scale-ups, or enterprise AI platforms
• Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work
• Comfortable owning systems end-to-end from data through model through deployment through monitoring
• Product-first engineering approach, not research-only profiles
• Advanced Python engineering skills with strong systems thinking and a focus on production quality
• Comfortable with fast iteration cycles and deploying models into live environments
• Ability to work directly and confidently with stakeholders and product owners
• Fintech or financial services experience is an advantage

Technical Skills

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

Why Join Us

• Work with one of the world's leading financial derivatives institutions
• Competitive salary plus performance-based incentives
• Access to a dynamic, international, and fast-growing environment
• Strong opportunities for career progression within a global financial group
• Be part of a business committed to innovation, excellence, and long-term growth