Job Summary:
Seeking a Senior AI Engineer to lead the technical design and execution of an AI-driven Anti-Money Laundering (AML) technology roadmap. The role will leverage strong Python expertise to architect robust, secure, and production-grade AI systems natively on AWS for real-time transaction analysis, complex financial crime network detection, and automated legal narrative generation. The engineer will help ensure AI systems meet applicable regulatory, safety, transparency, and data privacy requirements while collaborating with and providing technical guidance to the compliance engineering team.
Key Responsibilities:
• Design scalable AI systems and Agentic LLM workflows on AWS infrastructure to support real-time AML transaction analysis, link analysis, and network fraud detection.
• Architect classification and anomaly detection models to reduce false positives and improve operational efficiency for compliance analysts.
• Design and implement guardrails and transparency mechanisms to support compliant and explainable AI-driven decisions and generated documents.
• Ensure AI solutions incorporate applicable banking regulations, data privacy requirements, safety controls, and transparency practices.
• Oversee secure integration of vector databases, graph databases, and core banking APIs within AWS VPC environments.
• Design and support AI solutions capable of processing high-volume transaction data and identifying complex financial crime patterns.
• Guide the integration of machine learning, LLM, RAG, and agent-based technologies into compliance and fraud detection workflows.
• Provide technical mentorship to mid-level engineers and promote clean coding and engineering practices.
• Lead code reviews and contribute to technical standards and architecture decisions for the compliance engineering team.
Required Qualifications:
• 5+ years of software or AI engineering experience developing production-grade solutions using Python.
• Proven experience deploying machine learning models in high-throughput production environments.
• Expert-level understanding of transformer architectures, agent frameworks such as LangGraph or AutoGen, RAG architectures, and fine-tuning open-source models.
• Extensive experience scaling cloud-native AI workloads using AWS services such as Amazon SageMaker, AWS Lambda, EKS/ECS, and Amazon Bedrock.
• Strong experience designing and integrating secure AI systems within cloud environments.
• Experience working with vector databases and enterprise data integrations.
• Strong experience with graph databases such as Neo4j or Amazon Neptune for entity resolution or transaction network analysis.
• Strong understanding of AI security, transparency, guardrails, data privacy, and responsible AI practices.
• Experience working within Financial Services, FinTech, or Banking environments.
• Strong understanding of AML concepts and applicable regulatory requirements, including the Bank Secrecy Act and FinCEN guidelines.
• Strong technical leadership, mentoring, code review, and problem-solving skills.
