AI Engineer

The core responsibilities for the job include the following:

Agents that run finance workflows:

• Own end-to-end finance workflows as autonomous agents: invoice-to-pay, three-way matching and exception handling, GL close tasks, reconciliation, and vendor and compliance checks.
• Design tool and action surfaces over ERP systems, control flow, error recovery, human-in-the-loop checkpoints, approval and segregation-of-duties guardrails, and full auditability of every action taken on financial data via computer-use agents, documents, and file-based integrations using LangGraph, LangChain, or custom orchestration.

Memory and the finance knowledge graph:

• Own agent memory: context management (windowing, summarization, compaction), long-term memory, retrieval and forgetting policies, and cross-cycle state for long-running agents.
• Model the finance domain as a knowledge graph of vendors, invoices, POs, GRNs, GL accounts, contracts, approvals, and their provenance in a graph database (Neo4j or equivalent) alongside vector search, ensuring every output can be substantiated for finance and audit review.

Evaluation and model strategy:

• Build and extend our evaluation platform: golden datasets, workflow-level metrics, regression gates in CI, automated judging calibrated against human review, and drift detection on live traffic.
• No change is released without a measured evaluation delta. Work across model families (Anthropic, OpenAI, Google, and open-weight) and own routing, which model handles which step at what quality, cost, and latency point, with tiering, cascading, and provider failover.

Scale and platform:

• Own latency, throughput, and unit economics for agents and pipelines operating at large document and transaction volumes, maintaining p50/p95/p99 latency and cost-per-workflow targets as the customer base grows.
• Maintain the API surface (FastAPI, REST) that delivers these capabilities into our products, and mentor engineers working alongside AI systems.

Required Experience:

• Agents in Production: Demonstrated experience delivering a multi-step agent that performs actions in a production system, with a clear understanding of its failure modes and the controls used to contain them.
• Memory: experience designing the memory and context strategy for a long-running agent, including retention, summarization, and pruning decisions.
• Evaluation: hands-on experience building an evaluation harness, including dataset construction, metric selection, and calibration of automated judging.
• Multiple models: delivered systems on at least two model providers or families, with experience migrating a live workload between them.
• Scale: experience operating an LLM or ML system in production at significant request volume, with measurable before-and-after results.

Requirements:

• Degree in computer science, computer engineering, or a related field.
• Advanced proficiency in Python and modern AI/ML development practices.
• Hands-on experience with agent orchestration frameworks (LangGraph, LangChain, OpenAI Agents SDK), or with custom orchestration built on model APIs directly.
• Working knowledge of graph data modelling and a graph database (Neo4j, Neptune, ArangoDB): schema design, query (Cypher or Gremlin), and judgement on when a graph is preferable to a relational or vector store.
• Strong API and data engineering fundamentals (FastAPI, PostgreSQL, vector stores) and cloud-native deployment on GCP, AWS, or Azure.
• Sound judgement on where LLM-based approaches are appropriate and where deterministic methods are preferable.
• Our Stack: Python LangGraph / LangChain Anthropic, OpenAI, and Google model APIs; FastAPI; PostgreSQL with pgvector, Neo4j, and Elasticsearch; Java/Spring Boot; React; GCP and AWS; Docker; an in-house evals platform; and in-house OCR.
• Familiarity is helpful but not a prerequisite.

Preferred / General Requirements:

• Finance, accounting, audit, or ERP (SAP) domain exposure, or a strong interest in developing domain depth.
• Computer-use or browser automation agents; GraphRAG and ontology design; document AI/OCR; fine-tuning or serving open-weight models.
• LLM tracing and observability (LangSmith, Langfuse, OpenTelemetry); enterprise AI security, governance, and compliance frameworks.
• Regular use of AI coding agents in day-to-day development; a significant share of our codebase is authored this way.