Senior AI Engineer

🕒 June 12

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Control Risks

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1975

📦 Logistics

📣 Marketing

✈️ Travel

💰 Private Equity Round on 2011-03

Logistics • Marketing • Travel

Control Risks is a global specialist risk consultancy firm. It helps organizations navigate and succeed in a volatile world by providing strategic advice and services in areas such as security risk management, operational and protective security, organizational resilience, crisis response, digital risks, and more. The company offers insights into political and country risks, ethics, compliance, and governance, as well as forensic and ESG (Environmental, Social, and Governance) services. Control Risks aims to ensure that its clients are prepared for and can efficiently handle various risk factors impacting their business operations globally.

📋 Description

• Own and evolve the platform’s knowledge graph data model — entities (Company, Person, Community, Episodic), relationships, and temporal attributes • Implement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring • Use Graphiti (Zep AI’s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates • Design and enforce graph schema standards, ensuring 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 that orchestrate LLM calls, web search, database lookups, and graph writes • Integrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment • Build retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers • Implement the graph interrogation layer — a conversational interface for compliance analysts to query the knowledge graph without writing Cypher • Manage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, structured outputs, and cost/latency optimisation • Build and maintain Python-based data ingestion pipelines reading from Databricks, connecting to external APIs (sanctions lists, corporate registries, Polixis, Vantage), and writing to Neo4j • Implement NER (named entity recognition) 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, ensuring the AI layer is independently deployable and testable • Stay current with the fast-moving agentic AI and knowledge graph literature — evaluate new frameworks, models, and techniques for production applicability • Contribute to architectural decisions with evidence: benchmarks, prototypes, and documented trade-offs • Maintain Claude Code context files (CLAUDE.md, SKILLS.md) for the AI codebase, enabling AI-augmented development across the team • Write clear technical documentation in Notion covering design decisions, data models, and operational runbooks.

🎯 Requirements

• 3+ years of professional experience in AI/ML engineering, with at least 1 year working on knowledge graphs or graph-based data systems in production • Hands-on experience with Neo4j — data modelling, Cypher query writing, schema design, and performance tuning • Deep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini) — prompt engineering, structured outputs, function/tool calling, and cost management • Understanding the trade-offs between LLMs and smaller fine-tuned models (Hugging Face Transformers, sentence-transformers) — knowing when a purpose-built model outperforms a prompted general one on cost, latency, and accuracy • Experience building agentic pipelines — multi-step LLM workflows with tool use, memory, and state management • Python proficiency — you write clean, testable Python and understand async patterns for I/O-bound AI workloads • Experience with RAG architectures — vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications • Ability to read and apply AI research papers — you can take a paper on narrative extraction or entity disambiguation and turn it into a working prototype • Strong English communication skills — written and verbal • Direct experience or knowledge with Graphiti (Zep AI) or other temporal knowledge graph frameworks • Familiarity with entity resolution and disambiguation techniques at scale • Experience with NER pipelines, topic modelling, or information extraction from compliance or financial documents • Knowledge of the third-party risk, sanctions screening, or KYC/AML domain • Experience with Databricks, Delta Lake, or similar data lakehouse platforms • Exposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage) • Experience designing evaluation frameworks for LLM-generated structured outputs • Prior work in a RegTech, FinTech, or compliance-adjacent environment.