Job Description

The AI Engineer will be responsible for designing, deploying, managing, and optimising AI products, machine learning models, and agentic AI workflows. Leveraging the Azure ecosystem, the role will focus on delivering production-ready AI/ML infrastructure and applications that are scalable, secure, governed, and cost-effective.

The AI Engineer will support the development of Data & AI products that accelerate DLA’s ambition to enhance legal processes through innovative technologies, improve operational efficiency, and deliver data-driven insights for better decision-making. This role will work closely with platform engineers, data scientists, data engineers, and business stakeholders to move AI solutions from experimentation into reliable production use.

Main duties and responsibilities

• 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

About you

• Hands-on experience with Azure Databricks, Azure ML, and ideally 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 of both traditional ML models and LLM-based solutions
• Strong understanding of LLMOps practices, including deployment, monitoring, scaling, evaluation, governance, and cost control
• Experience building agentic AI workflows and orchestration patterns using frameworks such as LangChain, LangGraph, or similar
• Understanding of Model Context Protocol (MCP) or equivalent approaches for standardised integration between AI applications, tools, and enterprise data sources
• Strong Python engineering skills, including experience with libraries and frameworks such as PyTorch, Pydantic, LangChain, and LangSmith
• Experience with prompt orchestration, structured outputs, evaluation, and tool-calling patterns for LLM applications
• Knowledge of RAG patterns, vector search, and enterprise retrieval approaches
• Understanding of cloud governance, compliance, and responsible AI controls
• Good understanding of key Azure services such as Virtual Machines, Active Directory, Automation, and related cloud infrastructure
• Experience building and supporting ETL and workflow pipelines using Azure Data Factory, Databricks workflows, or similar
• Experience with containerisation and orchestration tools such as Docker and Kubernetes
• Experience with version control systems, particularly GitLab, and CI/CD pipelines
• Familiarity with Agile product development environments, including sprint planning, stand-ups, and retrospectives
• Strong understanding of data structures, transformation logic, and integration patterns
• Ability to communicate effectively with technical and non-technical stakeholders
• Collegiate, pragmatic, and delivery-focused, with a willingness to support broader team goals

Required Qualifications

• 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 and other Azure services (e.g., Azure ML, AI Foundry)