Job Description

About the role

We are hiring an AI Engineer to turn large language models into real, reliable product features on Azure for a next generation legal AI application. You will design and ship retrieval augmented generation pipelines, ideally with agentic patterns, and you will own the full path from experiment to stable API. Your work will include prompt design, retrieval and re ranking, citation integrity, and a strong evaluation loop so answers are accurate, sourced, safe, and fast. You will collaborate closely with the founding team and the CTO on architecture, delivery, and quality standards, and you will help set the technical bar for how we build and operate AI in production.



This role suits someone who enjoys building rather than only researching. You are fluent in Python and modern ML frameworks, comfortable with vector search, and confident using Azure services such as Azure OpenAI, Azure AI Search, Functions or Container Apps (Docker), Key Vault, and Application Insights. If you want ownership, clear impact, and the chance to shape a legal assistant that must stand up to real scrutiny, we would love to speak.

Key Responsibilities:

Design, build and ship production LLM features with clear prompts, tools and retrieval Develop and maintain RAG pipelines, ideally with agentic patterns, including query rewriting, re ranking and citation integrity Build data ingestion and indexing for trusted sources, manage chunking, metadata and vector stores Own model evaluation and quality gates, with golden sets, regression tracking and live guardrails Turn experiments into stable APIs with clear contracts, authentication, rate limits and versioning Operate services in Azure, including Azure OpenAI, Azure AI Search, Functions or Container Apps, Key Vault and Application Insights Monitor reliability, latency, accuracy and cost, and drive continuous tuning Implement logging, observability and alerting, and keep audit trails for compliance Apply responsible AI and GDPR principles, including safe refusals, privacy and data retention Work closely with the CTO and product and legal stakeholders to translate requirements into testable behaviours and results Write clear documentation for pipelines, APIs, runbooks and operational playbooks Set up CI and automated evaluations so every change is tested before release, and support code reviews

Required Skills & Experience:

Strong background in machine learning, data science and applied AI Proven experience shipping LLM applications to production with RAG pipelines, prompt design, re ranking and citation integrity Solid Python, plus modern AI and ML libraries such as PyTorch, TensorFlow, Hugging Face, LangChain or LlamaIndex Vector search in practice, for example Azure AI Search, Pinecone, Postgres with pgvector on Supabase, Weaviate or Qdrant API design and integration, including secure REST and OAuth, and connecting to third party legal or content sources such as GOV.UK, ACAS, or commercial providers like Thomson Reuters Practical Law or Westlaw Proficiency with Azure services such as Azure OpenAI, Azure AI Search, Functions or Container Apps, Key Vault and Application Insights Experience building chatbots or copilots and automation in the Microsoft ecosystem, for example Copilot Studio or Teams apps Data pipelines and dataset preparation, including ingestion, parsing, chunking, metadata and indexing Model evaluation in practice, with golden sets, regression tracking and live guardrails for safety and privacy Security minded with good knowledge of data protection and responsible AI in a UK setting Ability to translate business needs into clear technical plans and explain complex ideas simply Comfortable working with product and legal stakeholders in a fast moving environment Degree in a relevant field and about five years of related experience, or an equivalent track record

Preferred Qualifications:

Strong experience with cloud platforms, Azure preferred Familiarity with OpenAI SDK, Azure AI SDK, LangChain and Pandas Knowledge of enterprise data governance, compliance and responsible AI practices Microsoft certification such as Azure AI Engineer Associate Working knowledge of TypeScript and React for simple admin tools CI and monitoring experience, for example GitHub Actions and Application Insights Microsoft 365 integration experience, for example Teams or SharePoint * Prior experience in professional services such as accounting or consulting