Senior Machine Learning Engineer

Role Overview

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.

• 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 optimise 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, personalisation, and campaign optimisation

• 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, Tensor Flow, XGBoost, Light
  
  GBM, Hugging Face (Transformers, Datasets, Diffusers)
• LLM and GenAI:
  OpenAI and Anthropic APIs, Lang Chain, Llama Index; 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, Git Hub Actions, Git Lab CI, Evidently AI, Arize, custom observability stacks
• Cloud: AWS (Sage Maker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
• Data Stack:
  Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures

• 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

Multi Bank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.