Senior ML Engineer
Overview
Our client in the banking industry is looking for a Senior ML Engineer with strong hands-on experience in productionising and scaling Machine Learning, AI and Generative AI solutions. The ideal candidate should have a strong software/cloud engineering background, with expertise in Databricks, MLflow, Azure Kubernetes Service (AKS), Python, Docker, Kubernetes and MLOps.
Requirements
- Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science Masters or Doctorate will be an added advantage.
- Microsoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent).
- Databricks certifications (Data Engineer, Machine Learning Engineer, Generative AI Engineer).
- Kubernetes and containerisation certifications (CKA, CKAD or equivalent).
- DevOps, MLOps or Platform Engineering certifications.
- AWS or Google Cloud certifications will be advantageous.
- Machine Learning, Artificial Intelligence or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera or DeepLearning.AI will be an added advantage.
Responsibilities
- Productionise, deploy and monitor machine learning models and data science pipelines on Databricks.
- Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks.
- Develop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance.
- Deploy, optimise and manage open-source AI and machine learning models on Azure Kubernetes Service (AKS).
- Design, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications.
- Implement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments.
- Monitor model performance, drift, reliability and operational health in production environments.
- Partner with Data Scientists to productionise prototypes and enable business-ready solutions.
- Collaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards.
- Troubleshoot and resolve production issues related to models, pipelines, APIs and AI applications.
- Optimise AI and ML solutions for performance, scalability, cost and reliability.
- Contribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem.
- Mentor junior engineers and promote knowledge sharing across the team.
- Stay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies.
How to apply
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