Senior Software Engineer - AI & Data Platform
Salary: ₱150,000 – ₱160,000 per month
We are looking for an engineer to build and scale the software, data infrastructure, and AI systems behind our business. Our platform connects operational systems and marketplace data, powers analytics and internal applications, and enables AI agents to execute business workflows. We serve multiple brands from one platform, so automation and data quality directly determine how far we can scale without adding headcount.
You will work closely with our IT & Operations Lead to turn complex business problems into reliable production systems. This is a hands-on role with meaningful architectural ownership: you will design systems, write code, ship to production, and own the results.
ABOUT THE ROLE
Our platform brings together operational data and large volumes of public Amazon marketplace data to support executive decisions, internal operations, and AI-assisted workflows. It includes a data warehouse, reporting tools, business applications, marketplace integrations, and agents that act on that data.
The next stage means connecting more systems, handling more volume, and giving agents greater responsibility. We need reliable foundations: trustworthy data, clear system boundaries, observable workflows, and controls over how automated decisions become actions. Errors here do not happen once. A wrong number or a bad agent decision repeats across every brand we serve and every workflow that depends on it.
You will help shape that architecture and write a substantial share of the code. Your scope spans the data platform and the applications and agents built on top of it, from data quality and modeling to APIs, internal tools, and production workflows. You will also improve how we test, deploy, and build with coding agents.
What you’ll build and own
Data reliability and correctness. Make sure the data our business, reports and agents rely on is right. Validate what arrives from ingestion, define data contracts with the teams and partners who produce it, and build automated data tests, reconciliation and freshness and quality checks so discrepancies are caught before anyone acts on them.
Testing and production reliability. Build the right mix of unit, integration, contract, end-to-end and data tests across the platform. Own deployment, monitoring and incident response for your systems. Turn production failures into concrete improvements in design, safeguards and test coverage.
Platform architecture and business systems. Design and build the services, APIs, integrations and internal applications that support our operations. Define system boundaries, workflow state, caching, queues and failure handling. Keep data models and business logic clean, documented and portable. Document consequential decisions and make practical build-versus-buy recommendations.
Operational workflows and integrations. Connect our platform to Amazon and other business systems. Build workflows that move from insight to action, including catalog and listing management, operational reviews and approved updates to external systems. Make those actions auditable, safe to retry and recoverable when dependencies fail.
Agent systems. Build agents that carry out meaningful business workflows using tools, structured data and application services. Own orchestration, context and retrieval, persistent state, permissions, human approvals and recovery. Define where autonomy is useful and where a person must retain control. When agents work in sequence, add validation between steps so errors are caught where they start, not at the end.
Agent evaluation and observability. Build evaluation datasets and regression checks for agent behavior. Use release gates appropriate to each workflow’s risk, and monitor task success, output quality, cost, latency and failure patterns in production.
AI-native engineering. Help define how we build with coding agents: shared skills, clear specifications, repositories agents can navigate, and review standards that keep delivery fast and code maintainable.
What you’ll work with
Python, TypeScript, SQL databases and a data warehouse, LLM-based agents, marketplace APIs, and cloud infrastructure. You will work across backend services, data pipelines, and internal web applications. We will discuss the current architecture and technical trade-offs during the interview process.
What we’re looking for
Production ownership. You have taken substantial systems from design through deployment and ongoing operation. You can explain the decisions you made, the failures you encountered, and how the system evolved.
Strong data engineering. You are fluent in SQL, data modeling, and reliable pipeline design. You can diagnose query performance, write data tests, and reconcile operational or financial data across systems. You have worked with more than one database or warehouse engine.
Architecture and system design. You have evolved systems beyond their first version. You understand queues, concurrency, caching, consistency, and partial failure, and can choose an approach that fits the problem without overengineering it.
Applied AI experience. You have shipped LLM-powered workflows or tool-using agents to production. You understand structured outputs, retrieval, tool interfaces such as MCP, and human approval patterns. You can explain how you evaluated performance and managed failure, cost, and latency.
Engineering quality. You know how to test business logic, integrations, data transformations, and nondeterministic outputs. You can distinguish tests that establish correctness from tests that merely repeat the implementation.
AI-native development. Coding agents are part of your daily workflow. You use them to accelerate implementation, investigation, and testing, and take responsibility for understanding and reviewing the code you ship.
Python and TypeScript. You are strong in Python and comfortable delivering production features in TypeScript, including the services and interfaces needed to make your work usable.
Judgment and execution. You turn ambiguous business needs into a bounded design, explain trade-offs clearly, and deliver useful increments quickly. You know when to move fast and when correctness requires deeper investigation.
Communication. You communicate clearly in written and spoken English and work effectively with technical and operational teammates.
Nice to have
Experience with Amazon APIs or marketplace data, including advertising, settlements, and catalogs.
Experience with dbt, Airflow, Dagster, or comparable transformation and orchestration tools.
Experience building internal tools, analytics products, or approval-based operational workflows.
Front-end experience building web applications with React or a similar modern framework.
What great looks like in your first 6 months
You understand the platform’s major dependencies and have shipped an architectural improvement with a measurable impact on reliability, cost, or speed.
You have delivered a meaningful business workflow across its data, application, and integration layers.
Key metrics in your area reconcile automatically against their sources, with discrepancies surfaced for investigation.
The agents you own have repeatable evaluations, production monitoring, and clear approval and recovery paths.
You independently own a major area of the platform and help the team make better engineering decisions.
This role is not a fit if…
You need detailed tickets. You will get requirements in plain English and figure out the technical approach.
You build prototypes but have not owned systems in production.
You focus only on frontend, or only on analysis, without building backend and data systems.
How to apply
Send your CV or LinkedIn, your GitHub profile, and examples of work you have built: repositories, demos, or technical write-ups.
Include:
A system you owned in production and an important architectural trade-off you made.
A case where you reconciled data across systems or investigated numbers that did not tie out.
An AI-powered workflow you shipped and how you measured whether it worked.
If your work is proprietary, an anonymized technical write-up is welcome. We want to understand your contribution, your judgment, and the results.