Principal Machine Learning Engineer

Complyadvantage; Lisboa; Presencial; EUR 92 000 - 115 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Unlimited Time Off

Hybrid work model

Home office budget

Annual learning budget

Resumo da oferta

ComplyAdvantage is seeking a Principal Machine Learning Engineer to lead the engineering build‑out of ML and agentic AI across our AML/KYC and Fraud platform. You will design company‑wide MLOps and agentic AI platforms, build new models, and drive the engineering bar for production systems.

You will report to the VP of Engineering and collaborate with Data Science leadership to align AI strategy, governance, and implementation across data governance, security and compliance requirements.

Qualificações

• Experience building, training and productionising ML models at scale.
• Strong production Python skills and software engineering fundamentals.
• Experience leading architecture of MLOps platforms and related tooling.

Responsabilidades

• Lead architectural design and implementation of ML/agentic AI platforms.
• Translate roadmaps into scalable engineering deliverables with governance compliance.
• Set engineering standards for code quality, evaluation, CI/CD, and reliability.
• Oversee end‑to‑end AI system development including LLMs, RAG, multi‑agent systems, graphs.

Conhecimentos

ML models at scale

Python

Kubernetes

Docker

GCP/AWS

LLMs

MLOps

Strong communication

Ferramentas

PostgreSQL

Yugabyte

Kafka

ArgoCD

Argo Workflows

Grafana Cloud

Descrição da oferta de emprego

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 our customers

About you

As a Principal Machine Learning Engineer with company‑wide impact, you will bring:

• Substantial experience building, training and productionising machine learning models at scale, including modern deep learning and large language model approaches.
• 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.
• Strong mathematical and statistical foundations. You can act as the company’s go‑to expert on rigorous, defensible application of techniques.
• Experience leading the architectural design of MLOps platforms: training pipelines, feature and vector stores, serving infrastructure, and drift and performance monitoring.
• Experience with cloud (GCP and AWS), containerised infrastructure (Kubernetes, Docker, ArgoCD, Argo Workflows), event brokers (Kafka) and modern data engineering workflows (batch, streaming, ETL).
• 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.
• Excellent written and verbal communication. You can engage senior stakeholders and engineers, and produce technical documentation people can act on.
• A track record of coaching ML engineers at every level and of helping Recruiting improve the hiring process.

Nice to have

• Experience applying ML, LLMs and agentic AI in AML, KYC, fraud, TegTech or another regulated domain.
• Familiarity with knowledge graphs, entity resolution, link analysis and temporal reasoning over relationship data.
• Experience designing evaluation frameworks for LLM and agentic systems, including safety, accuracy and operational guardrails.
• External profile in the ML community: speaking at conferences, contributing to publications or open‑source projects.

Benefits

• Equity as we want you to have a part of what we are building
• Unlimited Time Off Policy- A work‑life balance and focus on our well‑being are critical to keeping us performing at our best
• 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
• You will also get a new starter budget to kit out your home office
• Opportunity to work on innovative projects with smart‑minded people keen to share their knowledge and continuously improve
• Annual learning budget (prorated based on start date) to drive your performance and career development

The base salary range for this role is 92,000-115,000 EUR + equity and benefits. The actual pay may vary based on factors such as location, experience, and skills.