Applied AI Engineering Lead - Global-Scale Systems

Firmable; Sydney; Presencial; AUD 210.000 - 280.000; Jornada completa

Descripción de la vacante

Firmable seeks an Applied AI Engineering Lead to own scalable data resolution and graph engineering. You will lead a senior, multi-country team with hands-on engineering at ~80% and leadership at ~20%.

The role focuses on challenging, real-world problems at scale, pushing novel methods when libraries fall short, and delivering cost-aware, end-to-end solutions. You will design end-to-end architectures, manage trade-offs, and shape the platform for agentic retrieval and multi-step runs.

Formación

• 8+ years building production data or AI systems, including at least one you architected from scratch and then operated.
• Very large scale data architecture design and delivery across billions of records; know bottlenecks and costs.
• Deep in two of the following: record linkage at scale, property graph modelling, hybrid retrieval, multi-step agent systems.
• Production experience with agent systems that write to and read from a persistent store and include provenance, diffs, rollbacks.
• Agentic retrieval and traversal planning: cost caps, context assembly, stop rules; avoid naive RAG on entity-rich data.
• Strong Python: typed, async, Pydantic for schema enforcement, FastAPI for services.
• Distributed processing at scale: Spark/Snowpark/Ray/Dask with deliberate partitioning and optimization.
• Graph modelling in Neo4j/Memgraph and AWS services plus Terraform/CDK for infra.

Responsabilidades

• Own the architecture end-to-end, from raw source through resolution, storage, to interfaces called by agents and customers.
• Lead a small senior team across multiple countries, setting the technical bar and daily operating norms.
• Design agent systems that write to and read from a persistent store with guarded writes, provenance, and diffability.
• Separate planning from execution using state machines; enable deterministic replays and human approvals.
• Orchestrate multi-step runs with cost and latency attribution, measuring overall trajectory not just final answers.
• Define evaluation benchmarks and establish AI-native development workflows as standard practice.
• Collaborate with Product to translate ideas into prototypes and architectural decisions quickly.

Conocimientos

Python

Async programming

Pydantic

FastAPI

Agent systems

Distributed processing

Cross-functional comms

AI-native development

Herramientas

Spark

Snowpark

Ray

Dask

Neo4j

AWS

Terraform

Temporal

Descripción del empleo

Firmable seeks an Applied AI Engineering Lead to own scalable data resolution and graph engineering. You will lead a senior, multi-country team with hands-on engineering at ~80% and leadership at ~20%.

The role focuses on challenging, real-world problems at scale, pushing novel methods when libraries fall short, and delivering cost-aware, end-to-end solutions. You will design end-to-end architectures, manage trade-offs, and shape the platform for agentic retrieval and multi-step runs.