Senior AI Engineer

Design, build, and deploy fraud detection agents in C# using Microsoft Agent Framework.

• Implement multi-agent workflows, tool calling, and human-in-the-loop approval processes.
• Develop event-sequence detection to identify fraud patterns in account activities.
• Build deterministic rules and risk-scoring logic for MVP, including case prioritization.
• Transition detection logic from deterministic rules to generative AI methods.
• Integrate anomaly reasoning, alert summarization, and agent-led investigation.
• Ensure every alert includes explainability for fraud analysts (events, scores, evidence).

AI Training Agents

• Build AI training agents to generate and curate labeled fraud scenarios and synthetic datasets.
• Develop feedback loops using analyst decisions to tune rules, prompts, and models.
• Create evaluation frameworks to measure detection rates, false positives, and reasoning quality.

Knowledge Graphs

• Design, build, and maintain knowledge graphs in Neo4j/Stardog linking accounts, devices, transactions, etc.
• Model graph ontologies and create queries (Cypher, SPARQL) to detect shared identifiers and fraud rings.
• Integrate knowledge graph context into agent reasoning via retrieval and graph-based features.

Engineering & Delivery

• Collaborate with Solution Architect and Business Analyst to translate fraud scenarios and data requirements into solutions.
• Build data pipelines to ingest authentication, recovery, account, and transaction events.
• Apply software engineering best practices: version control, CI/CD, automated testing, monitoring, cost & latency management.
• Ensure compliance with security, privacy, model risk, and audit requirements for financial crime systems.
• Document design decisions and hand over solutions to support teams.

12+ years in software engineering; recent experience with generative AI applications and AI agents on cloud.

• 1+ year hands-on with Microsoft Agent Framework (agent orchestration, tool calling, workflows).
• Advanced skills in C# and .NET (async programming, dependency injection, API design).
• Experience with Azure OpenAI/Microsoft Foundry and Azure cloud services.
• Proficiency in knowledge graphs (Neo4j/Stardog, Cypher, SPARQL, ontology design).
• Experience with event/time-series data and correlating events across sources.
• Understanding of fraud and anomaly detection methodologies.
• Experience designing evaluation/testing for LLM and agent outputs.
• Ability to work with business/data/tech stakeholders and in regulated environments.

Desirable Skills

• Experience with graph data science (community detection, link prediction, graph embeddings).
• Python skills for data science/model experimentation.
• Familiarity with identity/document verification signals.
• Knowledge of model risk management, responsible AI, and UK/EU AI regulations.
• Relevant certifications (Microsoft Azure AI Engineer Associate, Neo4j Certified Professional).