ComplyAdvantage
Financial Services
PrincipalMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Principal Machine Learning Engineer at ComplyAdvantage. Skills: Machine Learning, Agentic AI, MLOps, LLMs. Lead architectural design of MLOps platforms. Lead architectural design of agentic AI platforms”
What You'll Achieve.
Detect money laundering; Detect terrorist financing; Detect sanctions evasion; Detect financial crime; Evolve financial crime knowledge graph
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
Substantial experience building ML models, Deep production Python experience, Software engineering fundamentals, Mathematical and statistical foundations, Experience leading MLOps platform design, Experience with cloud (GCP, AWS), Experience with containerised infrastructure, Experience with event brokers (Kafka), Experience with modern data engineering workflows, Experience turning brief into ML work, Excellent written communication, Excellent verbal communication, Track record of coaching ML engineers, Track record of helping Recruiting
Nice to Have
Experience applying ML in AML/KYC/Fraud, Familiarity with knowledge graphs, Familiarity with entity resolution, Familiarity with link analysis, Familiarity with temporal reasoning, Experience designing LLM evaluation frameworks, Experience designing agentic systems evaluation, External profile in ML community
What You'll Do.
Lead architectural design of MLOps platforms
Lead architectural design of agentic AI platforms
Implement MLOps platforms
Implement agentic AI platforms
Translate ML roadmaps
Translate agentic AI roadmaps
Ensure production builds adhere to governance
Ensure production builds adhere to compliance
Set engineering bar for code quality
Set engineering bar for evaluation design
Set engineering bar for operational standards
Set engineering bar for CI/CD pipelines
Lead end-to-end engineering build-out of AI systems
Represent ComplyAdvantage at conferences
Represent ComplyAdvantage at industry forums
How You'll Work.
Team & Collaboration
Work with VP Engineering; Work with Director Data Science; Engage senior stakeholders; Engage engineers
Communication Scope
Technical documentation
Process & Methodology
Roadmaps
Full Job Description
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,
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