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

- **Company:** TWG, INC.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $290,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Data Integration, Information Technology Audit, Blockchain, Systems Integration, Management of Software Versions, Machine Learning Operations
- **Published:** August 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9340a0a63adeac26

## About the Role

* 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

## Description

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.

What you'll do:

 * 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.


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