Senior Internal Automation Engineer
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. We need to preserve all substantive information, remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions). So we need to strip sections like "About Volta", "Background Checks", "Equal Opportunity", "Accessibility", "Candidate Privacy Notice", "Note to Recruitment Agencies". Also remove duplicated lines. We need to keep "About Volta" maybe? It's not listed as a required heading but could be considered part of description. The instruction says include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text. "About Volta" is substantive, describing company. Should we include it? The list of example headings didn't include "About the company", but we could still include as
About Volta
with paragraph. However they said usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). They didn't forbid other headings, but they gave examples. Safer to include About Volta as a section? Might be considered extra but not prohibited. However they said "Prefer a few substantive sections over many thin ones." Might be okay to include About Volta as a brief paragraph before About the Role. But the instruction: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." So we can include About Volta as a heading. We need to output only HTML fragment, no /, no markdown fences. Let's extract content: About Volta paragraph: "Volta is the category-defining, fully vertically integrated AI infrastructure platform – from capital to clusters to software, under a founder-led enterprise. Our mission is The Utility of Compute™: AI infrastructure as dependable and available as electricity, for every organization that needs it. Launched with a $10B strategic partnership with one of the leading frontier AI labs, a Series A led by Andreessen Horowitz, and a $5B AI Infrastructure Fund, Volta is building the infrastructure layer of the AI era from the ground up. We are 100+ people across London, Palo Alto, and New York, with rapid growth expectations to hundreds." About The Role: paragraph describing role: "Volta builds and operates large scale GPU compute infrastructure for frontier AI customers. Growing this fast produces a long tail of manual internal work: invoices retyped into banking portals, expense reports chased by hand, spreadsheets reconciled every month, accounts provisioned ticket by ticket. Each task is small. Together they cost the company real time, and they multiply with every new sit and every new hire. This role removes that work permanently. You sit with the person who owns a process, understand what it actually does, then build the integration that runs it against banking and accounting systems, Microsoft Graph / Office, Slack, Jira and Confluence, and the HR and identity stack. Most of the answer is well built API code with sound authentication, error handling, escalation, audibility and observability. Some of it uses models, for classification, extraction, or matching, where that is the right tool rather than the fashionable one. Knowing the difference is a large part of the job. Much of this work lands inside the financial reporting path, and Volta is building toward SOX compliance alongside its existing ISO 27001 and SOC 2 obligations. An automation that touches invoices, payments, or reconciliation becomes part of the control environment the moment it goes live. That sets the bar: changes are reviewed, tested, and traceable, access is least privilege and segregated, every run leaves an audit trail an external auditor can follow, and nobody moves code into production by hand. You build to that standard from the first commit rather than retrofitting it under deadline. Regulation shapes the work in a second way. Volta is headquartered in the UK and operates across the EU, so anything you build with AI in it sits under the EU AI Act as well as UK and EU data protection law. Automation that touches hiring, performance, or other employment decisions carries the heaviest obligations, and the line between a helpful classifier and a regulated decision system is easy to cross without noticing. You are expected to recognize where that line runs, keep a person in the loop where the law requires one, document what you deploy, and bring Legal and Security in early rather than after the build. You sit inside Corporate IT at a fast-growing AI-forward company, which owns the tooling, accounts, and API access you build on, and which gives you engineers next to you for design discussion and code review rather than leaving you to mark your own homework. Security Engineering advises on access and compliance. The first internal customer is the accounting and finance team. After that the scope is the whole company: finance, IT, People, delivery, product, and the engineering teams for their own internal overhead. Very little here is customer facing. Initially, you are the only person doing this work full time, so you prioritize hard, ship small, and build things that keep running without you next to them." What You Will Be Doing: bullet list (we need
- ). Let's extract each bullet:
- Automate finance operations first: ingest received invoices into banking and bookkeeping systems, classify and match them, and close the loop on the exceptions
- Work directly with process owners across finance, IT, People, and delivery: take a described workflow and turn it into a specified, testable automation
- Work as part of Corporate IT: review their changes, have yours reviewed, and keep automation aligned with how the underlying systems are administered
- Build and run integrations against internal systems, including Microsoft Graph, Slack, Atlassian, banking platforms, and HR and identity tooling
- Own authentication and credentials for those integrations: OAuth flows, service principals, scoped tokens, secret rotation
- Build every finance touching automation to withstand audit: version controlled and peer reviewed changes, automated deployment with no manual production edits, least privilege service identities, segregation of duties preserved rather than automated away, and retained evidence of what ran, when, and on whose authority
- Work with Finance and Security Engineering on control design, so an automation replaces a manual control with a stronger automated one instead of quietly removing it
- Apply models where they earn their place, for document extraction, classification, or matching, and use conventional code everywhere else. Keep a human decision point where a control requires one
- Build agentic automations where a single scripted path will not hold: an agent that picks up a case, calls the tools it needs across Microsoft Graph, Slack, Atlassian and the finance stack, and stops at a human approval gate before anything with money or access impact. You define the tool surface, the permitted scope, and the blast radius, rather than handing a model a broad credential and watching what happens. Keep AI use inside its regulatory boundaries: automatically classify use cases correctly under the EU AI Act, apply transparency and human oversight where required, document deployed systems, and escalate anything touching employment decisions to Legal before it is built
- Instrument what you build so failures surface immediately and the state of any run is visible without reading logs
- Maintain what you deploy, including the unglamorous part where an upstream API changes and the process still has to run on Monday
- Set the patterns, repositories, and review practices that keep internal automation maintainable and auditable as more of it accumulates
- Push back when a process should be simplified or deleted instead of automated
What You Bring: bullet list (maybe combine Requirements, Qualifications, Skills). Let's extract each bullet:
- Production software development held to the standard of an engineering team, not scripting attached to an operational role
- Fluency across more than one software ecosystem. Internal systems arrive in whatever language, runtime, and SDK their vendor picked, and you are expected to work in what the problem needs rather than bending every problem toward one stack. Python and Go come up often here, neither is a requirement
- Deep practical work with third party APIs: pagination, rate limits, idempotency, retries, webhooks, and how an integration behaves when the far end is having a bad day
- Real command of authentication mechanisms, including OAuth 2.0, OIDC, service accounts, and API key and secret management
- Disciplined engineering practice that produces an audit trail as a by-product: source control, code review, automated testing, controlled deployment, and clear separation between who builds a change and who releases it
- Hands on experience building with LLMs, including tool use and structured extraction, plus the judgment to recognize when a model is the wrong answer
- Hands on work with agentic patterns: tool and function calling, MCP or equivalent tool interfaces, state and retry handling across multi step runs, and testing agent behavior against realistic cases before it touches a production system. Working awareness of the rules governing AI and automated decision making in a workplace context, at the level of knowing which use cases need legal review before a line of code is written
- Ability to sit with a subject matter expert, understand a business process well enough to implement it correctly, and translate it without needing them to specify the solution
- A track record of building reliable, secure, auditable systems rather than demos that work once
- Comfort as the only engineer on a problem: choosing what to build next and owning the outcome
Nice to Have: bullet list:
- Delivering AI systems under the EU AI Act: risk classification, technical documentation, transparency and human oversight obligations
- Building or operating systems in SOX scope: IT general controls, change management, segregation of duties, and producing evidence for external audit
- Finance systems experience: accounts payable, expense management, bank reconciliation, ERP integration
- Microsoft Graph, Slack, Atlassian, or identity platform APIs at more than a tutorial level
- Work under ISO 27001 or SOC 2
- Workflow orchestration tooling and the failure modes that come with it
- Document processing and information extraction at production quality
- A company scaling past 100 people, where processes are new and requirements move
What we offer: bullet list:
- Competitive salary based on the work you do here, not your previous salary
- Equity in Volta, giving you the opportunity to share in the company's long-term success
- Retirement/pension contributions
- Comprehensive health, wellbeing and insurance benefits
- Generous number of vacation days each year
Compensation Range: $192,062 - $269,590 (Pay section)
Schedule: not present, omit.
We also need to maybe include a short paragraph for metadata like location? Not required.
We must not include boilerplate like "Background Checks", "Equal Opportunity", etc.
Now produce HTML with headings
for each section. Potential sections:
About Volta
...
About the Role
...
What You Will Be Doing
- ...
What You Bring
- ...
Nice to Have
- ...
Benefits
- ...
Pay
$192,062 - $269,590
We could also combine Requirements/Qualifications/Skills into What You Bring (makes sense). The