Senior AI Systems Architect – Scalable Data & Graphs

Firmable; San Francisco (CA); On-site; USD 250,000 - 360,000; Full time

Job summary

Firmable seeks an Applied AI Engineering Lead to own and scale entity resolution and graph systems. You will lead a small senior team and drive architecture across extraction, resolution, and retrieval at global scale.

This role is ~80% hands-on engineering and ~20% team leadership, with direct influence on architectural decisions and research-driven methods to handle massive data volumes.

Qualifications

• 8+ years of production data or AI systems experience including a from-scratch architected system.
• Experience designing and running data/AI systems at billions-scale.
• Deep knowledge of two of: record linkage at scale, property graph modelling, hybrid retrieval, multi-step agent systems.
• Production experience with agent systems that write to/read from a persistent store with provenance and rollback.
• Expertise in agentic retrieval: traversal planning, context assembly, stopping rules, cost caps.

Responsibilities

• Own end-to-end system architecture from raw sources to interfaces used by agents and customers.
• Lead a small senior team and collaborate with product and platform teams.
• Design multi-step agent systems with state machines and deterministic replay.
• Balance hands-on engineering (~80%) with leadership (~20%), shaping architecture decisions.
• Ensure observability, testing standards, and cost-aware performance.

Skills

8+ years experience

Large-scale data architecture

Agent systems

Agentic retrieval

Python (typed, async, Pydantic)

Spark/Snowpark/Ray/Dask

Graph modelling (Neo4j/Memgraph)

AWS (S3, Lambda, ECS/EKS)

AI-native development workflow

Cross-functional communication

Tools

FastAPI

Pydantic

Terraform/CDK

Job description

Firmable seeks an Applied AI Engineering Lead to own and scale entity resolution and graph systems. You will lead a small senior team and drive architecture across extraction, resolution, and retrieval at global scale.

This role is ~80% hands-on engineering and ~20% team leadership, with direct influence on architectural decisions and research-driven methods to handle massive data volumes.