Senior Data Scientist (ML Engineering)
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.
Requirements
- 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.
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.
How to apply
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