Senior AI/Machine Learning Engineer – MANCOMM
Vacancy No VN996
Employment Type Permanent
Location London, England, United Kingdom
Overview
Chambers and Partners is transforming how the worlds leading legal professionals access insight and intelligence and is 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. You’ll collaborate closely with architecture, research, analytics, and product teams to 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.
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 categorise and organise 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 optimise ML models and LLMs to enhance performance, efficiency, and relevance for Chambers research, analytics, and product applications.
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, 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 minimisation, 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; contribute to technical specs and implementation plans.
- Build POCs to de‑risk architecture and showcase value; harden POCs into production services.
- Mentor and guide more junior engineers and data scientists; review code; contribute to technical design reviews; raise the collective standard of the team.
- Stay abreast of the AI/ML research landscape and legal‑tech/legal‑analytics domain to bring relevant innovations into our stack.
Skills and Experience
- Significant demonstrable experience in software engineering, with 2+ years building LLM/AI applications in production.
- Strong in Python, API design, asynchronous programming, and integration patterns.
- Proven ability to scale LLMs and other AI models for high‑volume, real‑world applications, including optimising inference, managing computational resources, and ensuring reliability and maintainability.
Programming & ML/LLM Frameworks
- Strong expertise in Python and relevant ML/LLM libraries/frameworks (PyTorch, TensorFlow, scikit‑learn).
- Hands‑on with LangGraph/LangChain, LlamaIndex or Semantic Kernel for orchestration (tools, agents, guards, structured I/O).
- Familiarity with Azure OpenAI and at least one open model stack (Llama/Mistral via vLLM/TGI/Ollama).
- Proficient with front‑end frameworks such as Angular for integration of AI‑powered applications.
- Experience with graph databases and knowledge graphs (Neo4j) for knowledge graphs and tool routing.
Cloud Deployment & MLOps
- Production deployments on Azure (AKS/ACI/Functions), CI/CD, and Infrastructure as Code (Bicep/Terraform).
Data & Information Management
- Experience with relational / semi‑structured database (MS SQL and Cosmos DB) and vector search indexing (Azure AI Search/pgvector/Pinecone/Weaviate/Milvus/Qdrant) and graph database (Neo4j or equivalent).
Governance, Security & Compliance
- Strong understanding of security, compliance, and ethical AI practices, including Key Vault, private endpoints, PII handling, RBAC, data governance, and bias mitigation.
Person Specification
- A passionate software engineer / data scientist with a history of driving his or her own technical and professional development.
- Worked in the media, publishing, research or a similar consumer focused industry. (Highly Desirable)
- Communication – able to clearly communicate complex technical subjects to team members and stakeholders.
- Able to lead, providing thought leadership in their domain.
- Attention to detail – focused on the finer details that make the difference.
- Delivery focus – pragmatic and driven to get solutions live.
- Be comfortable and motivated to deliver on personal KPIs.
- Proactive attitude – a self‑starter who seeks out opportunities for yourself and your team.
- Awareness of industry trends – such as challenges and best practices.
- Positive attitude – generating enthusiasm among team members.
- Able to build strong personal relationships and trust.
Equal Opportunity Statement
MANCOMM is an Equal Opportunity Employer. All qualified applicants will receive consideration.
