Senior AI Engineer
Posted Updated
Summary
Senior AI Engineer designing and deploying fraud detection AI agents in C#/.NET using Microsoft Agent Framework on Azure, including multi-agent workflows, human-in-the-loop approvals, and knowledge graphs (Neo4j/Stardog) to uncover fraud rings, plus evaluation frameworks for LLM/agent outputs in a regulated financial crime context.
Job Summary (List Format):
Fraud Detection Agents
- 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.
Required Skills & Experience
- 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).
Fraud Detection Agents
- 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.
Required Skills & Experience
- 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).