Senior Data Scientist (ML Engineering)

Senior Data Scientist (ML Engineering)

Role Overview

The Senior Data Scientist (ML Engineering) role is responsible for designing, building, deploying, and supporting next-generation machine learning, artificial intelligence (AI), and Generative AI solutions in production environments. The position focuses on operationalising models, developing AI applications and agents, and creating scalable platforms and services that deliver measurable business value. The role works closely with Data Scientists, platform teams, and business stakeholders to ensure AI solutions are secure, reliable, compliant, and production-ready.

Key Responsibilities

• Productionise, deploy, and monitor machine learning models and data science pipelines on Databricks.
• Build, deploy, and support AI Agents, Generative AI applications, and Retrieval-Augmented Generation (RAG) solutions.
• Develop and maintain reusable ML pipelines using MLOps best practices, 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 to expose AI and machine learning capabilities.
• Implement containerised solutions using Docker and Kubernetes to support scalable and resilient deployments.
• Monitor model performance, drift, reliability, and overall operational health.
• Collaborate with Data Scientists to transition prototypes into production-ready solutions.
• Work with cloud, security, infrastructure, and platform 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, reliability, and cost efficiency.
• Contribute to engineering standards, reusable frameworks, and best practices across the AI ecosystem.
• Mentor junior engineers and support knowledge sharing within the team.
• Stay up to date with advancements in AI, Generative AI, MLOps, Databricks, Kubernetes, and cloud technologies.

Requirements

Essential Skills & Experience

• Proven experience building, deploying, and supporting machine learning solutions in production environments.
• Strong expertise in Databricks Workflows, Model Serving, MLflow, and Mosaic AI.
• Hands-on experience with Azure Kubernetes Service (AKS).
• Advanced proficiency in Python, SQL, REST APIs, Docker, and Kubernetes.
• Strong understanding of MLOps, DevOps, and software engineering best practices.
• Experience implementing CI/CD pipelines and infrastructure automation.
• Knowledge of machine learning, Generative AI, Large Language Models (LLMs), RAG, and AI Agents.
• Experience deploying and managing AI and machine learning solutions on cloud-native platforms.
• Ability to productionise data science solutions and collaborate effectively with Data Scientists.
• Experience with distributed computing technologies such as Spark and large-scale data processing frameworks.
• Strong troubleshooting, monitoring, observability, and problem-solving capabilities.
• Excellent stakeholder engagement, communication, and cross-functional collaboration skills.

Desirable Skills & Experience

• Experience with open-source LLM deployment and optimisation.
• Exposure to platform engineering and cloud infrastructure automation.
• Experience delivering end-to-end AI and machine learning solutions from development through production.
• Knowledge of AWS or Google Cloud environments.
• Experience designing secure and scalable AI platforms and services.
• Relevant industry certifications in AI, Data Science, Cloud, Kubernetes, or Platform Engineering.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuarial Science, or a related field.
• Master's degree or Doctorate will be advantageous.
• Relevant certifications such as:
  • Microsoft Azure (AZ-104, AZ-305, AI-102, or equivalent)
  • Databricks Data Engineer, Machine Learning Engineer, or Generative AI Engineer
  • Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD)
  • DevOps, MLOps, or Platform Engineering certifications
  • AI, Machine Learning, or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera, or DeepLearning.AI

Behavioural Competencies

• Strong analytical and problem-solving skills.
• Effective written and verbal communication abilities.
• Ability to translate technical concepts into business outcomes.
• Strong collaboration and stakeholder management skills.
• Self-driven and proactive approach to work.
• Adaptability and ability to thrive in fast-paced, technology-driven environments.
• Commitment to continuous learning and professional development.
• Ability to mentor and support the growth of junior team members.

What We Offer

• Opportunity to work on cutting-edge Artificial Intelligence, Machine Learning, and Generative AI initiatives.
• Exposure to modern cloud-native technologies including Databricks, Azure, Kubernetes, and MLOps platforms.
• Collaborative environment with Data Scientists, Engineers, and business stakeholders.
• Opportunities for technical leadership, innovation, and career growth.
• Participation in impactful projects that deliver measurable business value at scale.