Product Infrastructure Engineer, Data & Agent Systems

About the role

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.

Responsibilities

• Data infrastructure and model migration
• 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
• Agent execution systems
• 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

Requirements

• 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

Qualifications

• 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

Skills

• Strong coding ability in TypeScript, Python, Go, Rust, or similar
• Experience with async jobs, queues, workflow engines, retries, idempotency, and failure recovery
• Experience with relational databases, schema design, migrations, and data integrity
• Experience with data pipelines, ingestion systems, transformation layers, or backend services around complex data models
• Experience with data quality checks, validation gates, audit logs, approval flows, or data quality checks
• Experience with workflow engines, internal platforms, automation infrastructure, or developer tools
• Experience with multi-tenant SaaS systems
• Experience with Postgres, Drizzle, Supabase, Temporal, Dagster, Airflow, Celery, BullMQ, Sidekiq, or similar systems
• Experience with LLM agents, tool-calling systems, or human review workflows

Benefits

The role offers a competitive salary range of $180,000 — $200,000 yearly, stock options, and remote work is not allowed.

Pay

$180,000 — $200,000 yearly

Schedule

Not specified