Core Platform Engineer

OnTrade is building an AI platform for wealth management that connects enterprise data, applications, and agents. We help financial institutions build workflows around their own business practices, bringing AI into the work their teams do every day.

We’re looking for a hands‑on engineer to build the core platform and product capabilities that make this possible. You’ll work across the engineering stack, distributed systems, data architecture, backend services, and user‑facing applications. Your work will give customers and our Customer Engineering team a reliable foundation for building and operating intelligent workflows at scale.

We particularly value experience building infrastructure on AWS, Google Cloud, or Azure. You should also bring strong data and application development skills, practical experience building AI applications, and confidence using AI coding agents in your daily work.

You’ll own technical problems from architecture through production, working closely with product and Customer Engineering to turn real customer needs into capabilities that serve the entire platform.

Build the core platform.

Design and implement shared services, APIs, and execution frameworks for data integration, applications, and AI agents. Build abstractions that support different firms’ needs while keeping the platform coherent and maintainable.

Own cloud infrastructure and production operations.

Build and evolve the infrastructure behind OnTrade’s platform, including compute, networking, storage, identity, and deployment automation. Improve observability, failure recovery, performance, and cost as usage grows.

Develop the platform’s data foundations.

Build systems for ingesting, transforming, storing, and serving enterprise data. Evolve domain models and ontology capabilities that give applications and agents a consistent understanding of entities, relationships, and business context. Make data quality, lineage, and access controls part of the architecture.

Ship product capabilities end to end.

Take features from an initial problem through backend services, APIs, and user experiences. Work with product to make complex data and AI capabilities understandable and useful to the people relying on them.

Build reliable AI applications and agent infrastructure.

Connect models to enterprise data, tools, and workflows. Design for permissions, durable execution, retries, and human review so agents can perform useful work within each firm’s controls.

Measure and improve AI behavior.

Build evaluations, tracing, and feedback loops that expose failures and guide improvements. Make deliberate tradeoffs between model quality, latency, and cost, and test how changes affect real workflows.

Turn implementation needs into reusable capabilities.

Partner with Customer Engineering to identify recurring requirements and platform gaps. Build connectors, tools, and configuration patterns that make each subsequent customer implementation easier to deliver and operate.

Advance how we build software.

Use AI coding agents throughout development, debugging, testing, and code review. Help establish practices that let the team move quickly while maintaining clear ownership of correctness, security, and maintainability.

You may have deeper expertise in some of these areas than others. We value a strong foundation, evidence of what you’ve built, and the ability to learn across infrastructure, data, AI, and product development. Experience in wealth management or financial services is helpful, but not required.

An engineer with real infrastructure depth.

You’ve built and operated cloud systems and understand what happens beneath an application’s APIs. Experience engineering infrastructure or platform services at a cloud provider is especially…