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We are seeking a capable and solutions-focused AI Engineer to join our growing AI Team. This role blends Generative AI, Machine Learning (ML), Microsoft-centric software engineering, and integration, and is suited to someone who has delivered AI-powered solutions and is ready to deepen their expertise in an enterprise environment. You will work across Azure, M365, Copilot, Copilot Studio, Databricks, and modern engineering frameworks to deliver production-grade AI services under agreed architecture and security standards. The role includes hands-on experimentation and rapid proof-of-concept (PoC) development, with guidance from senior engineers on governance, risk, and best practice. You will help scale AI safely and sustainably across our property and legal services domain.
Job Responsibility:
Build and enhance GenAI solutions using Azure OpenAI, Azure AI Services, and Copilot extensibility, following agreed patterns
Build conversational assistants and workflow automations using Copilot Studio and the Power Platform
Contribute to experimentation, prototyping, and PoC development to evaluate AI capabilities and feasibility
Support evaluation and integration of third-party AI tools or APIs where required, working with the team to meet governance and security requirements
Develop and operationalise ML models with appropriate review and documentation
Translate prototypes into robust services, collaborating with engineering colleagues to meet non-functional requirements
Create ML pipelines and automate lifecycle workflows using team tooling and standards
Assist with monitoring, optimisation, escalating risks and issues where appropriate
Build AI-enabled services and APIs using .NET/C#, Python, Azure Functions, and REST under guidance on patterns and quality
Use Visual Studio, VS Code, GitHub, and Azure DevOps for CI/CD
Contribute to deployments using infrastructure-as-code templates (e.g., Bicep or ARM), with support from senior engineers
Implement integrations with Logic Apps, Service Bus, and Event Grid using secure, approved patterns
Work with data and AI platforms such as Databricks and Azure data services
Build and maintain pipelines for ML and LLM workloads, partnering with data engineering where needed
Integrate internal and external systems using secure authentication and authorisation approaches
Follow responsible AI, privacy, and security standards, contributing to risk assessment and documentation
Conduct evaluation, guardrail and regression testing
Document architectures, PoCs, and technical decisions
Collaborate with stakeholders to refine use cases and deliver solutions
Requirements:
Hands-on experience building solutions with Azure AI Services and integrating them into applications and/or data solutions
Working knowledge of Azure OpenAI and common GenAI patterns (prompting, evaluation, basic RAG)
Some experience with ML delivery (training, packaging, deployment, monitoring) in a live environment
Proficiency in either C#/.NET or Python, plus SQL fundamentals
Understanding of vector search concepts (embeddings, chunking, retrieval) and secure API integration
Experience using Git-based workflows and CI/CD pipelines (e.g., GitHub or Azure DevOps)
Strong communication and problem-solving skills, including clear technical documentation
Nice to have:
Building RAG solutions end to end and improving answer quality
Copilot Studio connectors/actions and Power Platform ALM
Databricks, Fabric, or Azure ML in a delivery setting
Working knowledge of infrastructure-as-code and cloud deployment practices
Understanding of REST API design and microservices patterns
Awareness of emerging agent patterns i.e., MCP/structured agentic architectures
Relevant certifications such as Azure AI Engineer Associate, Azure Data Scientist Associate, Power Platform Developer or Solution Architect, Databricks ML or Generative AI certifications, or Emerging AI and AI Solution certifications
What we offer:
Mentorship from Senior AI colleagues
Support in architecture and governance
Opportunity to deepen Azure AI / Copilot / ML expertise