machine learning engineer

DLA Piper is a global law firm that provides legal services across the Americas, Europe, the Middle East, Africa, and Asia Pacific. The firm focuses on cross-border projects, critical transactions, and high-stakes disputes while leveraging innovative technologies to enhance legal processes and improve operational efficiency.

• Design, develop, deploy, and optimise machine learning models and LLM-based solutions using Azure Databricks, Azure ML, or Azure AI Foundry;
• Build and maintain scalable LLM-powered applications, ensuring performance, reliability, and cost efficiency in production;
• Develop and support agentic AI workflows for autonomous or semi-autonomous task execution and orchestration;
• Build and maintain pipelines that support AI/ML workflows, including data preparation, experimentation, evaluation, deployment, and monitoring;
• Collaborate with platform engineers, data scientists, data engineers, and business stakeholders to integrate AI/ML solutions into production environments;
• Implement and optimise retrieval, prompting, tool-calling, and orchestration patterns for enterprise AI applications;
• Develop AI services and workflows using LangChain, LangGraph, or similar frameworks for multi-step reasoning and orchestration;
• Enable standardised tool and context integration across AI applications using MCP or similar interoperability patterns;
• Monitor, troubleshoot, and continuously improve models and AI workflows in production to ensure reliability, quality, and accuracy;
• Apply LLMOps and MLOps best practices across experimentation, versioning, deployment, monitoring, and lifecycle management;
• Ensure AI/ML solutions align with cloud governance, security, compliance, and responsible AI requirements;
• Document models, workflows, engineering patterns, and deployment processes to support reproducibility and knowledge sharing;
• Stay current with emerging AI/ML, LLMOps, and agentic AI capabilities and apply them pragmatically to improve existing solutions.

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML Engineering, or a related field;
• 5+ Years of experience in machine learning and data engineering;
• Proven experience with Azure Databricks, Azure ML, and Azure AI Foundry;
• Strong experience deploying and managing LLMs and machine learning models in enterprise cloud environments;
• Experience using MLflow for experiment tracking, model lifecycle management, and versioning;
• Strong understanding of LLMOps practices including deployment, monitoring, scaling, and governance;
• Experience building agentic AI workflows using frameworks such as LangChain or LangGraph;
• Strong Python engineering skills with PyTorch, Pydantic, and LangSmith;
• Knowledge of RAG patterns, vector search, and enterprise retrieval approaches;
• Experience with Azure Data Factory, Docker, Kubernetes, GitLab, and CI/CD pipelines;
• Understanding of cloud governance, compliance, and responsible AI controls;
• Ability to communicate effectively with technical and non-technical stakeholders;
• Nice to have: Understanding of Model Context Protocol (MCP), familiarity with Agile product development, experience with Azure Virtual Machines and Active Directory.

• No conditions specified