Product Infrastructure Engineer, Data & Agent Systems

Product Infrastructure Engineer

Truewind is building AI agents that help accounting teams close books faster and more accurately. Our agents read documents, prepare work papers, reconcile transactions, draft structured outputs, and operate across ERP and financial systems.

To make this reliable in production, we need a strong product infrastructure engineer who can build the data foundation and execution systems underneath the product.

This is a backend-leaning infrastructure role for someone who can work across production data models, correctness-sensitive workflows, and agent execution systems. It is data-first in the near term: the primary focus is migrating Truewind from legacy data models into cleaner, more durable domain models while keeping live customer workflows working. As that foundation gets stronger, the role also expands into the execution infrastructure that lets AI agents safely complete real work.

This is not a prompt engineering role, a pure analytics data role, or a pure Dev Ops/SRE role. It is a product infrastructure role for someone who has lived through messy production systems and can move between backend services, data correctness, workflow reliability, and product-facing infrastructure.

Truewind is in the middle of a major platform transition.

We are migrating from legacy schemas into cleaner domain models. These systems need to run side by side while we move product modules, preserve customer behavior, validate correctness, and avoid breaking production workflows.

Financial data has very little margin for silent error. A missing transaction, duplicated record, stale sync, or incorrect mapping can cascade into a wrong close. You will work with data from ERPs, banks, spreadsheets, PDFs, file uploads, and customer-provided documents that arrives in inconsistent formats and needs to be normalized, validated, audited, and made useful before humans or agents act on it.

At the same time, our agents are becoming more capable. They need reliable execution infrastructure: long-running jobs, retries, work spaces, artifacts, review flows, logs, traces, and failure recovery.

In a larger company, this might be split across data platform, product infrastructure, and agent runtime teams. At our stage, we need someone who can work across these layers without losing sight of correctness or product impact.

You will work directly with the engineering and product team on infrastructure that is already in production with real customers. The work sits between backend engineering and data infrastructure, with a stack that includes Type Script, PostgreSQL/Supabase, Drizzle, queue and workflow systems, cloud infrastructure, and Python or similar tools where they are the right fit for data and automation work.

You will help move Truewind from legacy data models to cleaner, more durable domain models while the product stays live.

This includes:

• Building and maintaining data pipelines that ingest, normalize, transform, and serve correctness-sensitive financial data
• Migrating customer-facing product modules from legacy schemas to new domain models
• Maintaining compatibility while legacy and new systems run side by side
• Designing schemas, repositories, services, APIs, and workflows around complex data models
• Writing migrations, backfills, validation checks, and test coverage
• Building data quality checks to catch missing, duplicate, stale, inconsistent, or incorrectly mapped records
• Improving observability around syncs, transformations, model transitions, and downstream product behavior
• Preserving tenant isolation, auditability, and correctness across data flows
• Creating internal tools that help engineers debug data pipeline and migration failures faster

You will also help make our AI agents reliable enough for real production workflows.

This includes:

• Building orchestration for long-running agent workflows, including queues, retries, cancellations, checkpoints, resumability, and failure recovery
• Designing workspace and artifact handling for documents, workbooks, logs, generated outputs, and intermediate files
• Building tool-calling infrastructure for agents to interact with files, APIs, documents, browsers, CLIs, and internal systems
• Implementing human review flows where users can inspect, approve, reject, or modify agent outputs
• Adding traces, logs, workflow state, and root-cause debugging tools so agent work is auditable and debuggable
• Introducing safer execution environments when agent tasks need to manipulate files, call tools, or run isolated code

• 4+ years of experience in product infrastructure, backend engineering, data infrastructure, or distributed systems

• Strong experience with relational databases, schema design, migrations, and data integrity

• Experience building data pipelines, ingestion systems, transformation layers, or backend services around complex data models

• Experience with async…