<h1><strong>What you will be doing</strong></h1> <p>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.</p> <p>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.</p> <p>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.</p> <h1><strong>Scope of the role</strong></h1> <p><em>Scope & Key Responsabilities</em></p> <ul> <li><em>Architectural Leadership</em>: 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.</li> <li><em>Strategic Execution</em>: 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.</li> <li><em>Engineering Rigor</em>: Set the engineering bar across the organization for code quality, rigorous evaluation design, operational standards, and CI/CD pipelines.</li> <li><em>Advanced AI Implementation</em>: 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.</li> </ul> <p><strong>Our Tech Stack:</strong></p> <ul> <li>Our technology stack is designed to run on public cloud architectures, notably AWS and GCP</li> <li>Development is organised around Kotlin and Python for our backend languages and TypeScript/ES6+React for our frontend stack</li> <li>We make substantial use of relational database technologies, notably Postgres, Yugabyte</li> <li>We also use an event-sourced model powered by Kafka for our communication bus and gRPC for our intra-service communication protocol</li> <li>We use modern observability solutions from Grafana Cloud and deploy our code using ArgoCD</li> </ul> <p>We have a strong emphasis on engineering excellence and strive to ship the best possible code and the best possible solutions to our customers</p> <h1><strong>About you</strong></h1> <p>As a Principal Machine Learning Engineer with company-wide impact, you will bring:</p> <ul> <li>Substantial experience building, training and productionising machine learning models at scale, including modern deep learning and large language model approaches.</li> <li>Deep production Python experience, strong software engineering fundamentals (design patterns, event-driven architectures, observability), and an instinct for what makes a model and a system maintainable in the long run.</li> <li>Strong mathematical and statistical foundations. You can act as the company's go-to expert on rigorous, defensible application of techniques.</li> <li>Experience leading the architectural design of MLOps platforms: training pipelines, feature and vector stores, serving infrastructure, and drift and performance monitoring.</li> <li>Experience with cloud (GCP and AWS), containerised infrastructure (Kubernetes, Docker, ArgoCD, Argo Workflows), event brokers (Kafka) and modern data engineering workflows (batch, streaming, ETL).</li> <li>Experience turning a directing scientist's or product owner's brief into ML work that ships and delivers measurable value, and pushing back where feasibility, data quality or risk make stated goals unrealistic.</li> <li>Excellent written and verbal communication. You can engage senior stakeholders and engineers, and produce technical documentation people can act on.</li> <li>A track record of coaching ML engineers at every level and of helping Recruiting improve the hiring process.</li> </ul> <p><em>Nice to have</em></p> <ul> <li>Experience applying ML, LLMs and agentic AI in AML, KYC, fraud, TegTech or another regulated domain.</li> <li>Familiarity with knowledge graphs, entity resolution, link analysis and temporal reasoning over relationship data.</li> <li>Experience designing evaluation frameworks for LLM and agentic systems, including safety, accuracy and operational guardrails.</li> <li>External profile in the ML community: speaking at conferences, contributing to publications or open-source projects.</li> </ul> <p><strong>Benefits:</strong></p> <ul> <li>Equity participation in our innovative mission to combat financial crime</li> <li>Unlimited Time Off Policy to promote work-life balance and well-being</li> <li>We embrace a hybrid approach that requires employees to be in the office for two days a week. We strongly believe that this approach fosters collaboration and enables the building of meaningful relationships</li> <li>Opportunities for collaboration and career development with smart, like-minded professionals</li> <li>Annual learning budget to support professional growth</li> <li>A home office budget to support working from home</