Senior ML / AWS Engineer - Pipeline Hardening, Governance & Integrations

At TWG AI, we drive innovation and business transformation across a range of industries—including financial services, insurance, technology, media, and sports—by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees. 

We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development. 

You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation. 

At TWG, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses. 

You'll own the parts of the systems that make them trustworthy and connected: hardened batch pipelines, model governance and lineage, and the external data integrations the systems depend on. In a regulated setting this is not back-office work — the ability to show exactly how a model was trained, on what data, and why it flagged an account is a core requirement, and this seat owns it. The second seat arrives as the DeFi integration surface materializes: attribution vendors, chain-data providers, and the international exchange's market-metadata feeds.

• →Hardening and maintaining the batch pipelines and feature infrastructure the models run on. This is proven, high-value work: turning multi-hour hangs into completing jobs took real data-skew engineering (pre-aggregation, windowing, connection-pool and capacity tuning), and that discipline needs a permanent owner.
• →Model governance: versioning, lineage, reproducibility, and audit-ready records of how every model and flag was produced. We already stamp model-package version, scoring-run ID, and feature-snapshot time on every score, gate registration behind quality checks, and run a feature-contract check that stops a run rather than silently scoring on drifted features. This seat extends and defends that — across both estates.
• →The external integrations that feed the systems — surveillance-vendor feeds (Eventus, in first-wave AWS ingestion), screening providers (PEP/sanctions), KYC/identity sources (a live feed plus historical), market/event data, and, as the DeFi build ramps, blockchain-attribution vendors and chain-data providers.
• →The MLOps tooling that keeps deployments consistent and reversible.
• →Keeping the systems audit-ready as they grow, in partnership with data science and compliance.

• 5+ years of experience in data / ML pipeline engineering
• Hands-on MLOps: model registries, versioning, lineage, and reproducible deployment
• Experience building and maintaining third-party API and data integrations
• Production AWS experience
• Comfort working to governance and audit requirements; regulated-domain experience a plus

This is an onsite position based out of our Santa Monica, CA or New York, NY offices.