Senior AI/Machine Learning Engineer

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.

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• 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.
  

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• 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.
  

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• 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.
  

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• 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.
  

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• 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