AI / ML Engineer

About K3 Advisory Group K3 Advisory Group is a UK professional services group with 18 trading subsidiaries and more than 1,200 staff. The Group spans corporate finance, tax, restructuring and insolvency, legal, financial planning, and technology-enabled advisory services.

All technology delivery must balance the pace required to exploit AI, data and automation with strict expectations around governance, regulatory obligations, security-by-design, audit trails, and client confidentiality.

Group Technology builds shared platforms, data foundations and AI-assisted products that scale across these businesses while accommodating local variation. Engineers in this team work close to commercial outcomes, with direct visibility of advisors, partners and clients.

Role Purpose

Design, build and operate practical AI capabilities that help K3 users interrogate business data, automate analysis and reduce manual effort — while preserving data security, auditability and human oversight.

The role is explicitly about applied AI in a regulated professional services context, not research. Success looks like AI-assisted features that advisors use day-to-day, that ground their answers in approved data, that fail safely, and that can be defended in front of internal audit, compliance and (where relevant) regulators.

Key Responsibilities

AI Capability Engineering

• Build AI-assisted features using approved model providers, orchestration frameworks and internal platform patterns.
• Design safe tool-calling workflows over governed data sources, including read-only query tools, metric lookup tools, and schema/context tools.
• Support document intelligence, financial analysis, variance explanation, forecasting support and guided Q&A use cases.
• Develop model and prompt versioning, telemetry, model monitoring and usage reporting.

Evaluation, Guardrails & Safety

• Implement prompt, tool and model evaluation suites to test answer quality, safety, tenancy and regression risk.
• Build guardrails that prevent models from choosing unauthorised client context, accessing raw data outside approved contracts, or exposing sensitive values in outputs.
• Define and maintain golden datasets and test scenarios that reflect real K3 workflows across different subsidiaries.
• Operate kill-switches, rate limits, fallback paths and human-in-the-loop checkpoints for higher-risk use cases.

Data & Platform Collaboration

• Work with data engineers to define semantic contracts, metric definitions and approved analytical functions that AI features can rely on.
• Work with full-stack engineers to integrate AI features into web applications, dashboards and workflow screens.
• Contribute to the Group’s responsible AI standards, including documentation of model cards, intended use, known limitations and review history.

Stakeholder Engagement

• Explain model behaviour, limitations and risk controls to non-technical stakeholders — including partners, compliance and risk functions.
• Translate business questions into measurable AI capabilities with defined inputs, outputs and evaluation criteria.
• Provide pragmatic guidance on where AI is and is not the right tool, including when conventional analytics or process automation would be safer or more effective.

Required Experience & Skills

• 3–5+ years applying AI, ML or data science techniques in production or business-critical environments.
• Strong Python experience, including pandas and modern ML/AI libraries.
• Experience with LLM application development, including tool calling, retrieval-augmented generation (RAG), evals, prompt management or agent-style workflows.
• Experience with SQL, analytical datasets and governed data access patterns.
• Familiarity with model monitoring, experiment tracking, model registry or broader MLOps practices.
• Understanding of privacy, security, audit logging and data minimisation in AI systems.
• Ability to translate business questions into measurable AI capabilities and clearly explain limitations to non-specialists.

Desirable Experience

• Experience with document intelligence, NLP, financial data, forecasting or classification problems.
• Exposure to regulated environments (financial services, legal, audit) and an appreciation of the additional controls this demand.
• Experience with vector databases, semantic search, or hybrid retrieval architectures.
• Familiarity with OpenRouter, Azure AI ser…