Conversica is seeking a Staff Backend Platform Engineer to set the technical direction for the production backend systems that power our next-generation AI platform. This is a senior individual contributor role with platform-wide scope, focused on the architecture and engineering standards that make our agentic AI possible: integrations, data ingestion, messaging infrastructure, orchestration, and the data foundations our agents depend on.
This role operates with a high degree of autonomy and technical authority. You will shape platform architecture across multiple teams, set engineering standards that the broader backend organization adopts, and drive the long-term technical direction of our backend systems.
What You'll Do
Set the technical direction for Conversica's backend platform, owning architecture decisions that span integrations, data, messaging, and orchestration across the entire system
Drive cross-functional initiatives that touch multiple engineering teams, partnering with AI Engineering, Product, and Integrations leadership to shape platform strategy and roadmap
Define and evolve org-wide engineering standards for backend service design, data modeling, integration patterns, observability, and operational excellence
Identify systemic reliability, scalability, and performance risks across the platform and lead the engineering response
Mentor senior and mid-level engineers across the backend organization, raising the technical bar through design reviews, written technical guidance, and pairing on hard problems
Represent the backend platform in technical strategy discussions with executive leadership and contribute to multi-quarter roadmap planning
Build and ship production code yourself; this is a hands-on individual contributor role, not a pure architect role
What You'll Build
Integration platform: inbound and outbound data processors, partner APIs, webhook ingestion, CSV bulk imports, sync logic, and resilient retry/backoff patterns for unreliable external systems
Messaging infrastructure: email (AWS SES), SMS (Twilio), and chat (OpenAI Chatkit) delivery pipelines including throughput management, deliverability, suppression, and audit trails
Platform services: the Campaign, Playbook, Flows, Contact, Knowledge, and Reporting services that compose our domain mode
Data foundations: Postgres schema design, data models for contacts, conversations, campaigns, and configuration. Storage patterns optimized for both transactional workloads and agent retrieval
Orchestration and event flows: FastAPI services, Celery workflows, EventBridge-driven event handling, queue management, and the action dispatch layer that executes agent decisions across CRM, calendar, and notification systems
Success in this Role
In your first 6 to 12 months in this role:
You’ve established the integration, data, and messaging patterns that other backend engineers build against, and the platform’s engineering standards are visibly stronger because of you.
You’ve led at least one cross-team technical initiative end to end, including the consensus-building, design, delivery, and operational handoff.
You’ve raised the operational maturity of the platform, measurably improving reliability, performance, or developer leverage in systems other teams depend on.
Qualifications and Experience
Equivalent of 8+ years of professional software engineering experience with significant focus on backend platform, integrations, or data infrastructure - including hands-on responsibility for systems running in production at scale.
Track record of leading cross-team or org-wide technical initiatives from conception through production, including the soft work of building consensus and unblocking other engineers
Proven experience deploying and operating production backend systems at scale
Strong proficiency in Python, including production experience with FastAPI, Celery, and similar async/queue-based frameworks
Deep experience with Postgres including schema design, query optimization, and operating Postgres in production
Experience working with large datasets and navigating real-world data quality challenges across heterogeneous source systems
Experience setting engineering standards (service design, data architecture, operational practices) that other engineers adopted and built on
Experience designing and operating integrations with third-party SaaS systems including handling rate limits, schema drift, and partial failures
Strong background in modern cloud-based architectures, particularly AWS
Active use of modern AI coding tools (Claude Code, OpenAI Codex, Cursor, or similar) as part of your development