Backend Engineer - AI Platform (Remote)
About the Role
We’re hiring a Backend Engineer (AI Platform) remote to build the core infrastructure behind Revax’s AI-native platform for professional services. In this role, you will design, optimize, and scale systems that power multi-agent drafting, knowledge graphs, and auditable AI workflows used by accountants, tax advisors, and consultants at leading firms. This is not a “ticket-taking” role; you’ll work directly with the founders, own critical backend services end-to-end, and help define how AI systems are built responsibly in regulated domains.
If you enjoy designing clean APIs, scaling Python services, and building systems that actually ship to paying customers, this Backend Engineer (AI Platform) remote role is for you.
What You’ll Do
- Build and own core backend systems.
- Build and optimize databases for complex, real-world data.
- Design and implement APIs using FastAPI for AI workflows and product features.
- Build and scale LangGraph-based multi-agent systems with checkpointing, retries, and observability.
- Integrate and scale vector search, knowledge graphs (Neo4j), and associated retrieval pipelines into production services.
- Deploy and operate GPU-backed services for AI workloads (e.g., Inference, embeddings, or agent pipelines) in production.
Scale & Operate Production Infrastructure
- Deploy services on GCP (Cloud Run / GKE) or equivalent cloud infrastructure.
- Improve performance, reliability, and cost efficiency (latency, throughput, retries, backoffs).
- Implement security-first patterns: auth flows, service-to-service auth, IAM, and access controls.
- Help prepare the platform for enterprise customers (SSO, auditability, compliance ready patterns).
Collaborate Closely with Founders & Product
- Shape system architecture and API design alongside full-stack and ML engineers.
- Translate ambiguous product ideas into robust backend implementations.
- Iterate quickly based on real customer usage and feedback.
Requirements
- 2-3 years building backend systems for real users.
- Production experience with FastAPI (or similar modern Python frameworks).
- Solid understanding of API design, PostgreSQL, and distributed systems fundamentals.
- Experience deploying and operating services on cloud infrastructure (GCP, AWS, and Azure).
- High ownership mindset: you care deeply about reliability, correctness, and outcomes.
Nice to Have
- Experience with LangGraph, RAG pipelines, vector databases, or knowledge graphs.
- Familiarity with Neo4j or graph-based data modeling.
- Experience working on AI-native or agentic systems.
- Comfort working in early-stage startups with ambiguity and speed.
- Experience deploying and working with GPU services for AI workloads.
Bonus
- Exposure to professional services, finance, tax, or regulated workflows.
- You actively use modern AI coding tools (Cursor, Claude Code, etc.).
- Strong instincts for developer experience and internal tooling.
What We Offer
- Real ownership over core backend infrastructure.
- Work directly with founders on high-impact technical decisions.
- AI-native product with real customers and immediate feedback.
- Modern stack: Python, FastAPI, LangGraph, Postgres, Supabase, Neo4j, GCP.
- Meaningful early-stage equity: we hire owners, not passengers.
- A lean, low-ego, high drive team that ships fast and cares about their work.
This role offers a unique opportunity to work on an AI-native platform with real-world impact, competitive compensation, and equity options.
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Who Will Succeed Here
Proficient in Python and FastAPI, with hands-on experience in building scalable backend services that integrate with AI systems and knowledge graphs, demonstrating a strong understanding of asynchronous programming.
Self-motivated and disciplined remote worker who thrives in a fully remote environment, capable of managing time effectively and collaborating with cross-functional teams to deliver high-quality software solutions.
Analytical mindset with a solid background in distributed systems and database management, particularly with PostgreSQL and Neo4j, able to design efficient data models and optimize system performance.
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