Principal Machine Learning Engineer

What you will be doing

We are looking for an exceptional Principal Machine Learning Engineer to lead the engineering build-out of ML and agentic AI across our AML/KYC and Fraud platform. Our products use ML, LLMs and agentic systems to extract entities, risks and relationships from millions of structured and unstructured sources, to score customer, transaction and fraud risk, and to power our real-time financial crime knowledge graph.

As a Principal MLE you will be a senior technical leader who builds the systems that bring our ML and agentic AI work to production. You will report into the VP of Engineering, working in alignment with the strategic direction set by the Director of Data Science, who owns AI/ML and data governance direction at ComplyAdvantage. Your remit is execution: the architectural design of our company-wide MLOps and agentic AI platforms, the build-out of new models and agent systems, and the engineering bar across all of it. You will also represent ComplyAdvantage at conferences and industry forums.

Your impact will shape how ComplyAdvantage uses ML across the company, and through that, how our customers detect money laundering, terrorist financing, sanctions evasion and other financial crime. Your work will help evolve a financial crime knowledge graph that spans public and private data, and is helping our customers make financial crime a thing of the past.

Scope of the role

Scope & Key Responsabilities

• Architectural Leadership: Lead the architectural design and implementation of our company-wide MLOps and agentic AI platforms, covering training, evaluation, serving, feature/vector stores, and agent orchestration.
• Strategic Execution: Translate the ML and agentic AI roadmaps set by the Director of Data Science into scalable engineering deliverables, ensuring all production builds closely adhere to established data governance frameworks and compliance standards.
• Engineering Rigor: Set the engineering bar across the organization for code quality, rigorous evaluation design, operational standards, and CI/CD pipelines.
• Advanced AI Implementation: Lead the end-to-end engineering build-out of AI systems pioneered and prototyped by Data Science, including LLMs, retrieval augmented generation (RAG), multi-agent systems, and graph neural networks.

Our Tech Stack

• Our technology stack is designed to run on public cloud architectures, notably AWS and GCP
• Development is organised around Kotlin and Python for our backend languages and TypeScript/ES6+React for our frontend stack
• We make substantial use of relational database technologies, notably Postgres, Yugabyte
• We also use an event-sourced model powered by Kafka for our communication bus and gRPC for our intra-service communication protocol
• We use modern observability solutions from Grafana Cloud and deploy our code using ArgoCD

We have a strong emphasis on engineering excellence and strive to ship the best possible code and the best possible solutions to...

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