Machine Learning Engineer

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Job description

Machine Learning Engineer
London/Hybrid (2-3 days worked remotely)
Salary up to £100,000 per annum

An exciting opportunity has arisen to join a well known, global organisation in the Law sector. This company, who are famed for their working culture are looking to develop their Data and AI products to improve operational efficiency and enhance legal processes through innovative technologies. The new ML Engineer will be integral to this process.As a Machine Learning Engineer, you will be responsible for deploying, managing, and optimizing Large Language Models (LLMs) and other machine learning models within the organization's cloud-based enterprise data platform. With a strong focus on the Azure ecosystem, the role will ensure the efficient and cost-effective utilization of cloud resources.

Key responsibilities:

• Design, develop, and deploy machine learning models using Azure Databricks
• Implement and optimize LLMs for various applications, ensuring scalability and performance
• Collaborate with DevOps Engineers, data scientists, data engineers, and other stakeholders to integrate ML solutions into production environments
• Develop and maintain ETL pipelines to support ML workflows
• Leverage experience with common MLOps frameworks for tracking of experiments, packaging code, and managing model life cycle
• Monitor and troubleshoot ML models in production, ensuring reliability and accuracy
• Stay updated with the latest advancements in ML and LLM technologies and apply them to improve existing solutions

Skills:

• 5+ years of experience in machine learning and data engineering
• Proven experience with Azure Databricks and other Azure services (eg, Azure ML, Azure Data Factory)
• Experience with Databricks Cluster setup
• Experience in leveraging MLflow for experiment tracking, model life cycle management, and versioning of both machine learning and large language models
• Knowledge of ETL pipelines (using Azure Data Factory, or Databricks pipelines) to ensure efficient and scalable data flows
• Experience with systems like Azure Synapse Analytics for managing large datasets and scaling ML workloads
• Experience in Python, PyTorch, TensorFlow or other DL frameworks
• Deep understanding of primary Azure services (Virtual Machines, Active Directory, Automation)
• Experience with containerization and orchestration tools (eg, Docker, Kubernetes)
• Experience with version control systems especially Gitlab and CI/CD pipelines

A Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field is desirable but not essential.

This role would suit someone with over 4 years of experience in Data Engineering/Machine Learning who is looking for a dynamic environment with plenty of progression.

If you are interested please apply for full details.

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