Multibank Group - Senior Machine Learning Engineer
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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 • Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers) • Machine Learning and AI: • 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 • LLM and GenAI: • MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks • MLOps and Production • Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP • Cloud: • Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures • Data Stack:
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
Benefits
• 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. • Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.
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