Senior ML Engineer at Praesignis

Job Description

• 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.
• The role will focus on taking ML and Data Science solutions from prototype through to production, building AI Agents, GenAI and RAG applications, deploying open-source LLMs, developing APIs and microservices and ensuring solutions are scalable, secure, monitored and reliable.

Key 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.

Core Deliverables

• Production-ready ML models and pipelines running on Databricks.
• AI Agents and business applications deployed on Databricks.
• Open-source LLMs and AI services deployed on AKS.
• Secure and scalable APIs exposing AI capabilities to consuming systems.
• Automated deployment, monitoring and governance processes.
• Reliable, scalable and compliant AI platforms supporting business outcomes.

Key Skills

• Databricks Workflows, Model Serving, MLflow and Mosaic AI.
• Azure Kubernetes Service (AKS).
• Python, SQL and REST APIs.
• Docker and Kubernetes.
• CI/CD and MLOps practices.
• Machine Learning and Generative AI.
• LLM deployment and optimisation.
• Cloud engineering and infrastructure automation.
• Monitoring, observability and troubleshooting.

Qualifications

• Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science Masters or Doctorate will be an added advantage.

Preferred Certifications

• 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.

Technical/Professional Knowledge

• Strong understanding of MLOps, DevOps and software engineering practices for machine learning platforms.
• Experience building, deploying and supporting machine learning solutions in production environments.
• Proficiency in Python and experience with SQL and API development.
• Experience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.
• Hands-on experience with Kubernetes, Docker and containerised application deployment.
• Experience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS).
• Knowledge of CI/CD pipelines, infrastructure automation and platform monitoring.
• Experience with distributed computing technologies such as Spark and large-scale data processing frameworks.
• Understanding of machine learning, large language models (LLMs), retrieval-augmented generation (RAG) and AI agents.
• Ability to productionise data science solutions and collaborate effectively with Data Scientists.
• Experience delivering end-to-end AI and machine learning use cases from development to production.
• Ability to translate technical concepts into business outcomes and communicate effectively with stakeholders.
• Strong written and verbal communication skills with the ability to work across cross-functional teams.
• Self-driven, adaptable and capable of thriving in a fast-paced, technology-driven environment.