🟢 At Dev.Pro , we work on projects that impact millions of people around the world — but we know it’s the people behind the tech who make it all happen. We truly value what makes each person unique and are building a workplace that’s inclusive, friendly, and supportive.
🟢 About this opportunity
We are seeking a Senior AI Platform Engineer to own the core platform layer that powers every agent in production — from multi-tenant agent configuration and schema architecture, to data pipeline contracts, evaluation harnesses, and customer onboarding automation.
This role sits at the intersection of backend platform engineering, LangGraph-based orchestration, and AI evaluation systems. You won't just build features — you'll own the infrastructure that makes all features possible: the agent orchestration graph, the customer configuration schema, end-to-end conversation logging, automated eval pipelines, and the scripts that deploy new customers in under 30 minutes.
If you love owning systems that other engineers depend on, ship at high velocity across a wide surface area, and take pride in leaving codebases cleaner than you found them — we want to hear from you.
🧩 Key responsibilities and your contribution
- Core Platform & Schema Architecture : own and evolve the core platform repository — the central Python package implementing our modular agent architecture across orchestration, tools, state, retrieval, configuration, and extensibility layers. Design and maintain customer configuration schemas including versioning metadata, lineage tracking, and component provenance fields aligned with our IP strategy. Implement backward-compatible schema extensions and ensure all active customer deployments upgrade without breaking changes. Enforce schema validation at all node inputs/outputs to prevent data drift across multi-tenant environments.
- Multi-Tenancy Architecture : build and maintain cross-client isolation across customer configuration, persistent state, and RAG pipelines. Implement multi-tenant tagging so conversation logs, eval datasets, and agent behaviors remain cleanly separated per customer. Design config-driven deploy parameterization to enable new customer onboarding without code changes — configuration-only deployment model. Ensure all platform changes are backward compatible — no code forking per customer .
- Data Pipelines & Conversation Logging : own the end-to-end conversation logging system — unified schema, row format, conversation capture, and metadata persistence to PostgreSQL and S3. Maintain and extend knowledge base ingestion pipelines : scraping, embedding, vector DB indexing, and retrieval validation for each customer deployment. Define and freeze data contracts between capture specifications and implementation — so downstream analytics, fine-tuning, and eval all receive consistent, well-structured inputs. Implement multi-tenant data tagging so every logged conversation is attribu