AI Engineer
Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Oct 10, 2026. Jeeves scores C on the Alion truth index.
Finance teams still spend most of their week on manual work: reading invoices and receipts, matching transactions, checking spend against policy, chasing approvals, and closing the books across several countries and currencies. AI can now do much of that work reliably, and Jeeves is building it into the core of our platform. That only matters if it works in production, with real money, for real customers.
As an AI Engineer at Jeeves, you will own whole workflows from end to end, not individual features. For example, you might own everything that happens between an invoice arriving and a payment going out: document extraction, matching, policy checks, approval routing, posting to the customer's ERP, and the evaluation and monitoring that tell us it's working. You'll decide how to build it, ship it, measure it, and keep improving it. You'll work closely with Product, Backend Engineering, and our customers' finance teams, but you won't wait for someone to hand you a spec.
This is a senior, hands-on role for people who are AI-native in how they work. You use coding agents and AI tools every day to move faster, and you know where they fall short. It isn't a research role, and it isn't a role for someone who wants a narrow, well-defined ticket queue.
Location: This role is remote based out of Argentina. #LI-REMOTE
What You'll Do:
Take a finance workflow (for example invoice-to-payment, expense review, or transaction reconciliation) and own it from problem definition through production and ongoing improvement
Design agentic and LLM-based systems that combine extraction, retrieval, reasoning, and tool use to complete multi-step work, with clear human-in-the-loop points for high-value financial decisions
Write the design docs, make the build-vs-buy and model choices, and set the success metrics for each workflow you own
Talk directly with customers and internal finance users to understand where the workflow breaks today and what "done" looks like
Build production-grade LLM pipelines: prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
Design retrieval and RAG components (chunking, embeddings, vector search, re-ranking) where a workflow needs grounding in customer documents or policies
Integrate AI services cleanly with Jeeves's backend: clear API contracts, retries, graceful degradation, and per-customer data isolation
Manage cost and latency across models, choosing the right model for each step
Build evaluation sets and automated evals for every workflow you own, and use them to catch regressions when prompts, models, or data change
Instrument AI components with logging, tracing, dashboards, and alerts so you know when quality drops before a customer does
Keep an audit trail of AI decisions that meets the standards of a regulated financial product
Set patterns, shared tooling, and practices that other engineers can build on
Show the wider team how to use coding agents and AI tools well in day-to-day engineering
Review peers' AI system designs and share what you learn openly
Own AI Workflows End to End
Build Reliable AI in Production
Measure, Monitor, and Improve
Raise the Bar for AI Engineering at Jeeves
Minimum Requirements:
7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
A track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction
Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar, including structured outputs, error handling, and evaluation
Experience designing agentic or multi-step AI workflows (tool use, orchestration, human review steps), or RAG systems with vector databases such as pgvector, Pinecone, or Weaviate
Strong proficiency in Python, plus solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider (AWS, GCP, or Azure)
Uses AI coding agents and tools (for example Claude Code, Cursor, or Codex) as a regular part of how they build, and can explain where they help and where they don't
Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
Professional fluency in English, written and spoken
Preferred Qualifications:
Experience in fintech, financial services, or another regulated industry where AI decisions must be accurate and auditable
Experience automating finance or back-office workflows such as accounts payable, expense management, reconciliation, or document processing
Has led a project or small group of engineers technically, even without formal management responsibility
Startup or scale-up experience building systems from scratch
Spanish or Portuguese fluency
Open-source contributions, technical writing, or talks on applied AI