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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Platform Engineer - Data Engineering - **Company:** SourceIn Advisors Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audio Signal Processing, Audit Trail, Software as a Service, Cloud Computing, Software Quality, Continuous Integration, Information Engineering, Disaster Recovery, Distributed Computing Environment, Python (Programming Language), Machine Learning, Queueing Systems, TypeScript, Management of Software Versions, Data Logging, Data Processing, Data Storage Technologies, Real Time Systems, ReactJS, Delivery Pipeline, Claude Code, Model Validation, Backend, Event Driven Architecture, Build Management, Low Latency, Machine Learning Operations, Api Design, Restful APIs, Model Inference, Data Pipelines - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25197_c1f8c0306d8bc485e1d3be1628d231ea ## About the Role * Senior engineering ownership: You've operated at Staff, Principal or equivalent scope, owning critical production systems and setting technical direction for other engineers. * Strong backend and distributed-systems expertise: You understand high-volume data processing, event-driven architectures, API design and data modelling. * Hands-on infrastructure experience: You've built infrastructure as code, CI/CD and observability capabilities yourself, rather than relying entirely on a dedicated platform team. * Early-stage startup experience: You've shipped products in an early-stage or high-growth environment where ambiguity, broad ownership and limited support functions were part of everyday life. This is essential. * Strong Python skills: You're comfortable building production-grade backend systems and have enough TypeScript and React experience to contribute to the product portal when needed. * ML systems fluency: You can discuss model evaluation, deployment, monitoring, accuracy, latency, and inference costs with an ML Lead. Research experience is not required. * Enterprise SaaS experience: You understand multi-tenancy, tenant isolation, access controls, and customer-specific entitlements. * A pragmatic approach to AI-assisted development: You're comfortable using coding agents, including Claude Code, while maintaining high standards for code quality, testing, security, and maintainability. Bonus points Experience in any of the following would be particularly valuable: * Auditability, data provenance or compliance-grade systems. * Speech and audio processing, including ASR, diarisation, streaming transcription or real-time systems. * Contact-centre technology, CCaaS, quality assurance or workforce optimisation. * Governance, risk and compliance (GRC) platforms or regulated enterprise environments. ## Description * Design and build the backend systems that power a scalable, multi-tenant SaaS platform. * Establish robust APIs, data models, and distributed processing architecture. * Make key technical decisions that will shape the platform as the company grows. Conversation pipelines and integrations * Build pipelines that transform raw conversations into structured, enriched, and scorable data. * Develop integrations with contact-centre platforms, ticketing systems and direct-upload workflows. * Help evolve the voice-processing architecture, including transcription and audio ingestion. Auditability and data provenance * Implement immutable versioning for scoring rubrics and configurations. * Build tamper-evident audit trails and preserve evidence throughout the conversation lifecycle. * Ensure historical results can be reproduced and explained, including why a particular interaction received its original score. Infrastructure and engineering excellence * Own infrastructure as code, CI/CD, environment management and deployment workflows. * Establish observability, logging, metrics, tracing, alerting, secrets management and disaster recovery. * Take responsibility for production reliability, incident response and operational readiness. Scale, performance and reliability * Build reliable data storage, retention and queueing systems. * Implement idempotent processing and reprocessing to prevent duplicate work and unnecessary AI inference costs. * Define service-level objectives and help scale the platform from early pilots to millions of interactions per month. Security and enterprise readiness * Build tenant isolation, permissions, customer-specific configuration, and entitlements into the platform. * Implement sensitive-data handling and redaction before conversations reach AI models. * Support evidence exports, SOC 2 readiness, and enterprise security reviews., * Founding-level impact: Work directly with the CTO and influence the platform's architecture from day one. * Real technical ownership: Build the systems, infrastructure, and engineering practices rather than inheriting established processes. * A meaningful problem to solve: Help enterprises adopt AI customer support with greater confidence, transparency, and accountability. * Broad engineering scope: Work across backend systems, cloud infrastructure, data pipelines, security, and product-facing functionality. * Room to shape the team: Establish the technical standards and foundations that future engineers will build upon.