Senior AI/Machine Learning Engineer

Chambers and Partners; City Of London; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading legal insights firm in the UK is seeking a Senior AI Engineer to design, develop, and deploy innovative AI and machine learning solutions. The role involves collaborating with various teams, building robust data pipelines and ensuring reliability and performance of AI applications. Candidates should have a strong background in Python, machine learning, and Azure services. This position offers the chance to work on exciting projects that transform legal research and product offerings.

Qualifications

• Hands-on experience with AI and machine learning solutions.
• Strong ability to design and deploy production-grade LLM applications.
• Familiarity with Azure cloud services.

Responsibilities

• Design and develop AI solutions for various legal applications.
• Build and maintain LLM applications and services on Azure.
• Collaborate with multiple teams to drive innovation.

Skills

Advanced NLP

Machine Learning

Data Pipeline Development

Python Programming

Azure Services

Agile Methodologies

Education

Relevant degree in Computer Science or similar

Tools

LangGraph/LangChain

Azure AI Search

CI/CD Tools (GitHub Actions, Azure DevOps)

Job description

Overview Chambers and Partners is transforming how the world's leading legal professionals access insight and intelligence and we're looking for a Senior AI Engineer to help drive that innovation. In this pivotal role, you'll design, develop, and deploy advanced AI and machine learning solutions that power our next generation of products and research capabilities. Collaborating closely with our architecture, research, analytics, and product teams, you'll bring creativity and technical expertise to the forefront of our data and technology strategy. This is a hands‑on engineering position focused on building and operating production‑grade LLM applications on Azure. You'll work on AI‑enabled and augmented intelligence solutions such as retrieval‑augmented generation (RAG), agentic workflows, and model integrations with a strong emphasis on reliability, performance, security, and continuous improvement.

Main Duties and Responsibilities

Data & Retrieval

• Build robust ingestion pipelines for PDFs/Word/Excel/Audio/JSON and semi‑structured sources.
• Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re‑ranking, and grounding.
• Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate).
• Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners' research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic search, entity recognition, and relationship extraction.
• Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy.

LLM & Machine Learning Application Engineering

• Design, build, and maintain traditional ML and LLM models and pipelines.
• Build LLM apps using LangGraph/LangChain: tools/function calling, structured outputs (JSON Schema), agents, and multi‑step reasoning.
• Implement ASR/TTS and multimodal where relevant (e.g., Whisper).
• Choose customization paths pragmatically: prompt engineering, system prompts, tools, adapters/LoRA, and selective fine‑tuning only when needed.
• Fine‑tune and optimize ML models and LLMs to enhance performance, efficiency, and relevance for Chambers' research, analytics, and product applications. Apply best practices for model adaptation, evaluation, and deployment, ensuring solutions are scalable, reliable, and aligned with business objectives.

Platform & Operations (LLMOps)

• Deploy and operate services on Azure (AKS/ACI/Azure Functions, API Management).
• Implement CI/CD (GitHub Actions/Azure DevOps), Infrastructure as Code (Bicep/Terraform), secrets via Azure Key Vault, private networking.
• Add observability: tracing/telemetry (OpenTelemetry, LangSmith), metrics, logs, cost and token usage monitoring alerts.
• Apply evaluation & QA: regression suites, offline eval sets/golden data, RAG evals (faithfulness, answer relevance, citation correctness), A/B tests, win‑rate testing.
• Ensure reliability: rate‑limit handling, retries/backoff, idempotency, circuit breakers, caching (e.g., Redis/semantic cache), fallbacks and degradations.

Governance, Safety & Security

• Enforce PII handling, data minimization, redaction, access controls, and auditability.
• Mitigate prompt injection/jailbreak risks; apply content filters/guardrails; track data residency.
• Establish and drive best practices for model versioning, reproducibility, performance monitoring, bias mitigation, data governance, and ethical AI use.
• Document architectural decisions, runbooks, and operational procedures.

Software Engineering & Collaboration

• Write clean, tested, maintainable code in Python (and optionally .NET).
• Apply SOLID, TDD/BDD where sensible, code reviews, refactoring, performance profiling. Collaborate in an Agile environment; contribu