AI/ML Engineer — Agentic Controls & Graph Analytics

Role purpose

Build the AI layer for Kednus’s audit-control platform, including AI copilots, anomaly detection, control mapping, graph analytics, fraud detection, and explainable compliance workflows.

Key responsibilities

• Develop AI-assisted control mapping, transaction classification, anomaly detection, and audit evidence review.

• Build agentic workflows that help finance and compliance users investigate exceptions, generate explanations, and prepare regulator-ready outputs.

• Develop graph analytics models for wallet/entity relationships, suspicious patterns, exploit detection, rug-pull detection, and network risk analysis.

• Design AI systems that are explainable, traceable, testable, and suitable for regulated enterprise environments.

• Create evaluation frameworks for model accuracy, hallucination prevention, control reliability, and human-in-the-loop review.

• Partner with security and compliance teams on AI model governance, algorithm auditing, and privacy-preserving AI.

• Support Kednus’s public positioning around Agentic AI, AI model governance, algorithm auditing, and full-lifecycle digital asset monitoring.

Required qualifications

• 5+ years of machine learning, applied AI, data science, or AI engineering experience.

• Experience with LLMs, agents, retrieval-augmented generation, model evaluation, prompt orchestration, or AI workflow automation.

• Strong Python skills and experience with ML frameworks, vector databases, orchestration tools, and production AI systems.

• Experience with anomaly detection, graph analytics, time-series analysis, classification, or fraud/risk modeling.

• Ability to build AI systems with explainability, monitoring, evaluation, and human review.

• Strong understanding of data privacy, model risk, and enterprise AI governance.

Preferred qualifications

• Experience with graph neural networks, blockchain analytics, fraud detection, AML, sanctions screening, or compliance automation.

• Familiarity with audit workflows, financial controls, or model governance frameworks.

• Experience with Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex, Semantic Kernel, MLflow, or similar tools.

• Experience deploying AI in regulated industries such as finance, healthcare, insurance, or government.

First 90-day success outcomes

• Build an AI control-mapping prototype.

• Define AI evaluation and governance standards.

• Deliver an exception-investigation copilot workflow.

• Establish explainability requirements for audit and compliance users.