Product Infrastructure Engineer, Data & Agent Systems

At the same time, our agents are becoming more capable. They need reliable execution infrastructure: long-running jobs, retries, workspaces, 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.

Team and stack

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 TypeScript, 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.

What you'll work on

1. Data infrastructure and model migration

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

2. Agent execution systems

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

You may be a fit if you have

• 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 jobs, queues, workflow engines, retries, idempotency, and failure recovery

• Strong coding ability in TypeScript, Python, Go, Rust, or similar

• Strong debugging instincts across data, backend, infrastructure, and workflow layers

• Good judgment around system boundaries, reliability, observability, and operational simplicity

• Comfort working in a startup where you may need to move between product features, infrastructure, data pipelines, and internal tooling

• Interest in building systems where AI agents do real work, not just generate text

Strong signals

• You have migrated a production system from one data model to another while keeping the product running

• You have built or maintained production data pipelines

• You have worked on systems where data correctness really matters

• You have designed validation gates, audit logs, approval flows, or data quality checks

• You have built workflow engines, internal platforms, automation infrastructure, or developer tools

• You have experience with multi-tenant SaaS systems

• You have worked with Postgres, Drizzle, Supabase, Temporal, Dagster, Airflow, Celery, BullMQ, Sidekiq, or similar systems

• You have worked with LLM agents, tool-calling systems, or human review workflows

• You enjoy turning messy real-world workflows into reliable, observable systems

Useful but not required

• Experience with ERP, fintech, billing, payments, reconciliation, accounting, or financial data systems

• Experience integrating with messy third-party systems such as ERPs, banks, payment processors, CRMs, file storage systems, or document APIs

• Experience with sandboxing or isolated execution technologies such as E2B, Daytona, AWS ECS, Docker, Kubernetes, Firecracker, gVisor, cloud IDEs, CI runners, or similar systems

• Experience building agent runtimes, tool execution platforms, secure execution environments, or notebook/code execution infrastructure

What makes this role different

This role sits at the intersection of data infrastructure, backend systems, product workflows, and agent execution. The center of gravity is not model behavior or demos. It is the production substrate that makes AI workflows reliable.

You will help build:

• data model migration paths

• backend services around durable domain models

• validation and observability for correctness-sensitive data

• workflow reliability for long-running jobs

• artifact, trace, and review systems for agent outputs

• safer execution environments when agent workflows need them

The best person for this role is likely a strong product infrastructure engineer: someone backend-capable, data-correctness-minded, and comfortable moving systems forward without breaking production.

Not a fit if

• You mainly want to write prompts

• You only want to work on model behavior

• You prefer demos over production reliability

• You are looking for a pure DevOps/SRE role disconnected from product and data modeling

• You are looking for a pure analytics or warehouse data engineering role

• You are uncomfortable working with complex data models

• You do not enjoy migrations, backfills, validation, and system cleanup

• You do not care about logs, traces, retries, idempotency, and observability

• You want a narrowly scoped role with only one type of problem

Why join now

Truewind is at the stage where the product is powerful enough that the platform underneath it matters more than ever.

We need engineers who can help turn AI workflows from impressive demos into reliable production systems. That means building the data foundation, migrating legacy modules carefully, and giving agents the execution layer they need to do real work safely.

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.