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About the Role
As an AI Engineer, you will own the AI and data layer of the platform, designing and implementing the knowledge graph architecture, building agentic pipelines for AI web research, and developing the graph interrogation layer to translate natural language compliance questions into structured graph queries. You will collaborate closely with the CTO and a small AI team, enjoying significant technical autonomy and direct influence over product evolution.
Responsibilities
- Own and evolve the platform’s knowledge graph data model, including entities, relationships, and temporal attributes.
- Implement and extend custom entity and relationship extraction pipelines using LMs with structured output validation and confidence scoring.
- Utilize Graphiti as the memory layer for managing episode ingestion, entity resolution, and graph updates.
- Design and enforce graph schema standards for consistency across data sources and ingestion pipelines.
- Build evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage.
- Design and implement multi-step agentic workflows orchestrating LLM calls, web search, database lookups, and graph writes.
- Integrate AI web search for real-time entity enrichment within agentic pipelines.
- Build retrieval-augmented generation (RAG) pipelines over the knowledge graph to translate compliance queries into Cypher and natural language answers.
- Implement the graph interrogation layer, a conversational interface for querying the knowledge graph.
- Manage LLM API integrations, including prompt engineering, structured outputs, and cost/latency optimization.
- Build and maintain Python-based data ingestion pipelines from various sources and writing to Neo4j.
- Implement NER and entity disambiguation logic for extracting structured facts from unstructured compliance documents.
- Develop topic modelling pipelines to classify extracted facts as knowledge graph relationship attributes.
- Integrate with the .NET backend via well-defined HTTP contracts.
- Stay current with agentic AI and knowledge graph literature, evaluating new frameworks, models, and techniques.
- Contribute to architectural decisions with evidence such as benchmarks and prototypes.
- Maintain Claude Code context files for AI-augmented development.
- Write clear technical documentation in Notion covering design decisions, data models, and operational runbooks.
Requirements
- 3+ years of professional experience in AI/ML engineering.
- At least 1 year of experience working on knowledge graphs or graph-based data systems in production.
- Hands-on experience with Neo4j, including data modelling, Cypher query writing, schema design, and performance tuning.
- Deep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini), including prompt engineering, structured outputs, function/tool calling, and cost management.
- Understanding of trade-offs between LLMs and smaller fine-tuned models regarding cost, latency, and accuracy.
- Experience building agentic pipelines with multi-step LLM workflows, tool use, memory, and state management.
- Python proficiency, including writing clean, testable Python and understanding async patterns.
- Experience with RAG architectures, including vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications.
- Ability to read and apply AI research papers to develop working prototypes.
- Strong English communication skills, both written and verbal.
- Direct experience or knowledge with Graphiti or other temporal knowledge graph frameworks is a strong advantage.
- Familiarity with entity resolution and disambiguation techniques at scale is a strong advantage.
- Experience with NER pipelines, topic modelling, or information extraction from compliance or financial documents is a strong advantage.
- Knowledge of the third-party risk, sanctions screening, or KYC/AML domain is a strong advantage.
- Experience with Databricks, Delta Lake, or similar data lakehouse platforms is a strong advantage.
- Exposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage) is a strong advantage.
- Experience designing evaluation frameworks for LLM-generated structured outputs is a strong advantage.
- Prior work in a RegTech, FinTech, or compliance-adjacent environment is a strong advantage.
Skills
- AI/ML engineering
- Knowledge graphs
- Graph-based data systems
- Neo4j
- Cypher
- LLM APIs
- Prompt engineering
- Structured outputs
- Function/tool calling
- Cost management
- Agentic pipelines
- Python
- Async patterns
- RAG architectures
- Vector search
- Hybrid retrieval
- AI research papers
- Graphiti
- Entity resolution
- Entity disambiguation
- NER
- Topic modelling
- Information extraction
- Databricks
- Delta Lake
- GCP
- Vertex AI
- Cloud Run
- Pub/Sub
- Cloud Storage
Location
- Global
Work Type
- Hybrid
- Remote
Experience Level
- 3+ years of professional experience in AI/ML engineering
- 1+ year working on knowledge graphs or graph-based data systems in production
Benefits
- Competitively positioned compensation and benefits package
- Discretionary global bonus scheme
- Hybrid working arrangements
- Direct responsibility
- Career development
- Opportunity to work collaboratively on fascinating projects
- Rewarding and inclusive global environment
About the Company
- Control Risks operates a discretionary global bonus scheme that incentivises, and rewards individuals based on company and individual performance.
- Control Risks supports hybrid working arrangements, wherever possible, that emphasise the value of in-person time together - in the office and with our clients - while continuing to support flexible and remote working.
- Working with our clients, from day one our people are given direct responsibility, career development and the opportunity to work collaboratively on fascinating projects in a rewarding and inclusive global environment.
- The Company is committed to further its diversity, inclusion and equity agenda and as an employee you have access to various business resource groups in the Company including the Company's LGBT+ network called MOSAIC, the Women's Network, and Synergy - a people network to celebrate and promote ethnic diversity at Control Risks.
Equal Opportunity
- Control Risks is committed to a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.
- If you require any reasonable adjustments to be made in order to participate fully in the interview process, please let us know and we will be happy to accommodate your needs.