Data Engineer (AI Infrastructure)

Tax Relief Advocates is hiring a Data Engineer (AI Infrastructure) to build and operate the data systems and services behind its AI platform. The role transforms financial and case documents into reliable structured data and supports AI, analytics, and operational systems through data pipelines, APIs, integrations, and platform engineering.

Responsibilities

• Build batch and real-time pipelines that bring in data from documents, APIs, CRM systems, and internal services
• Build document-processing systems on OCR and AI models that stay accurate, reliable, and affordable as volume grows
• Design data models across relational databases, document stores, and data lake storage, and change them safely as the product evolves
• Give AI/ML teams clean, structured, accessible datasets, including training data
• Build data quality checks and monitoring so bad data is caught before anyone relies on it
• Build and run APIs and background services, and own how they behave when the systems they depend on are slow, failing, or rate-limited
• Maintain integrations with our CRM and other third-party systems, and fix them when they break
• Help build real-time sales tools where speed and correctness both matter, such as lead routing and live caller lookup
• Contribute to secure, controlled data exports to external partners
• Maintain older services and migrate them to our primary cloud where it makes sense
• Support the internal web tools our case and sales staff use
• Write design docs for your own work and contribute to architecture decisions
• Work in our infrastructure-as-code and CI/CD setup, changing it as your work requires
• Protect sensitive personal and financial data in code, logs, configuration, and third-party integrations
• Mentor junior engineers and help improve our standards for code quality, CI, and releases, including guardrails for AI coding agents

Preferred

• Experience with our stack: Python, Azure, React and TypeScript, and Google Cloud (where some older services still run)
• Orchestration and data platform tools such as Dagster, Airflow, or Databricks
• Streaming systems such as Kafka
• Retrieval and vector search
• Distributed rate limiting or API gateway design
• CRM or third-party integrations with fragile authentication
• Regulated or PII-heavy domains such as tax or finance
• Using AI coding agents in production work
• Python
• Microsoft Azure
• React
• TypeScript
• Google Cloud
• Dagster
• Apache Airflow
• Databricks
• Kafka
• Retrieval systems
• Vector search
• Distributed rate limiting
• API gateway design
• CRM integrations
• Third-party integrations
• Authentication integrations
• Regulated industry experience
• Tax industry experience
• Finance industry experience
• PII data handling
• AI coding agents
• Production AI coding

Benefits

• Comprehensive health, dental, and vision insurance
• Supplemental benefits, including life insurance, flexible spending accounts, and more
• 401(k) with a 4% employer match

Company

• Tax relief and tax resolution services for individuals and businesses
• Irvine, California, USA
• https://tra.com/
• Founded in 2017
• 1001-5000 employees

Qualifications

• 2-3 years building production backend or data systems, or equivalent depth: you've owned production systems end to end, made the design decisions, and handled the incidents
• A degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• You treat engineering as more than programming. You frame the problem, weigh the options, write down the trade-offs, and choose the simplest design that works. You ship code that's tested, observable, and easy to change. You measure before you optimize, and you own what you build after it ships
• Deep experience in at least one backend language
• Strong SQL and data modeling across relational and document databases, and a clear sense of when to use which. You understand indexes and query plans, transactions and isolation, schema changes that don't break running systems, and keeping data consistent when it lives in more than one store
• You've built pipelines over structured and unstructured data (JSON, PDFs) that are safe to rerun and backfill, and you understand how to model data for analytics
• You've run LLM or OCR workloads in production and know how they behave: output that varies from run to run, structured output that needs validating, quality you have to measure, and latency and cost that grow with volume
• You understand how models are deployed and served: capacity and quotas, pinning versions and evaluating new ones before switching, rolling back, and falling back when a model or region is unavailable
• You understand rate limiting from both sides: how a client should handle a 429, how a service protects itself with quotas and backpressure, and the various 429 errors that can occur for LLM deployments
• You treat APIs, events, and schemas as contracts between services and teams: versioned, backward compatible, and clearly owned. You design for partial failure: timeouts, idempotent retries, delivery guarantees, and what to do with work that will never succeed
• Production experience on a major cloud (AWS, Azure, or GCP), running services on serverless and container platforms. You understand what sits underneath (networking, DNS, private connectivity, identity, and permissions) and can read, change, and deploy it as code through CI/CD
• You understand OAuth 2.0 and OpenID Connect, SSO and MFA, and how tokens are issued, validated, and expired. You follow current practice, such as short-lived credentials and service-to-service auth without shared secrets
• You design access around roles and least privilege, scope permissions to the resources they cover, and enforce them on the server, not just in the UI
• You understand the lifecycle of user and service accounts: provisioning, role changes, offboarding, and regular access reviews
• You keep secrets out of code and personal data out of logs, and you treat customer and company information as strictly confidential
• Clear design docs, PR descriptions, and incident write-ups
• Experience with our stack: Python, Azure, React and TypeScript, and Google Cloud (where some older services still run)
• Orchestration and data platform tools such as Dagster, Airflow, or Databricks
• Streaming systems such as Kafka
• Retrieval and vector search
• Distributed rate limiting or API gateway design
• CRM or third-party integrations with fragile authentication
• Regulated or PII-heavy domains such as tax or finance
• Using AI coding agents in production work
• 2-3 years building production backend or data systems, or equivalent depth: you've owned production systems end to end, made the design decisions, and handled the incidents.

Required skills

• AWS, Azure, or GCP, CI/CD, Data Modeling, Data Pipelines, Distributed Systems, Infrastructure as Code, LLM and OCR Workloads, OAuth 2.0 and OpenID Connect, Python, Rate Limiting, Role-Based Access Control (RBAC), SQL

Preferred skills

• CRM or third-party integrations with fragile authentication, Distributed rate limiting or API gateway design, Regulated or PII-heavy domains such as tax or finance, Retrieval and vector search, Streaming systems such as Kafka, Using AI coding agents in production work

Experience

• 2-3 years