Senior Engineer, AI & Automation Platform

About the role

We're looking for an engineer to build and own the automation that turns firm and client documents — spreadsheets, PDFs, decks, committee logs, due-diligence materials — into audit-ready institutional deliverables, including quarterly client reports and investment-committee memos. Every number has to foot, every factual claim has to trace to a source, and a person signs off before anything reaches a client or committee.

You'll draw the line between deterministic code and model judgment, keep the model's output honest once it's in the loop (multi-pass generation, critique, citation audit, full-source verification), and take systems from a local prototype to something running unattended on a server, reproducibly.

What you'll do

AI-assisted document automation

• Build ingestion pipelines across heterogeneous sources (multi-sheet workbooks, PDFs, decks with chart data trapped in raster images) into structured, verified output
• Keep computation, ranking/selection, reconciliation, and compliance checks deterministic; use a model only where judgment is genuinely required, with its output grounded and verified, not trusted
• Design conservative, traceable entity resolution — not silent fuzzy-matching
• Handle missing, malformed, or irrelevant inputs gracefully: fail loud and safe, and distinguish "flag and continue" from "stop for human review"

Multi-pass generation & human-in-the-loop

• Build generate → critique → revise → audit → verify pipelines, not single-shot prompts
• Verify absence-claims against the full source corpus, not just retrieved context
• Keep domain/analytical knowledge in structured, human-editable data files, not hardcoded logic — a different knowledge pack should change the output, not the code
• Design the draft/approve boundary and how sign-off gets recorded; every material figure and factual claim resolves to a machine-readable source
• (Stretch) Contribute to systems that learn this structure from example documents rather than having it hand-authored

Platform engineering

• Own reproducibility: same inputs → same output; next period's inputs work via configuration, not a rewrite
• Take systems from local scripts to server-deployed production — environment parity, secrets, scheduling, monitoring for unattended runs
• Cache expensive third-party extraction calls; re-run only when sources actually change
• Build a lightweight operator interface (kick off a run, review the output), with real testing and LLM-call observability

What we're looking for

• The ability to cleanly separate probabilistic AI behavior from deterministic business logic and validation — knowing what must never be delegated to a model
• Strong engineering fundamentals; production data pipelines shipped, not just prototypes
• Real experience building LLM-integrated systems with grounding/verification, and a clear sense of where models fail
• Experience with multi-pass/self-critique LLM workflows, and the judgment to know when that's worth it
• Experience extracting data from unstructured/semi-structured documents, including chart data that only exists as pixels
• Demonstrated testing/verification judgment — how you know output is correct, how you'd catch a regression
• Comfort taking a system from a laptop to unattended production
• Clear, honest technical communication about what a system does and doesn't do

Nice to have

• Asset management, real estate, or other regulated financial services experience
• Vision-capable/multimodal model experience for image/scanned-document extraction
• Third-party document-parsing/OCR API experience and cost-aware caching
• Data governance/audit/compliance exposure
• We run primarily on Azure — familiarity with Azure OpenAI, Azure AI Search/Document Intelligence, or Azure Functions/Container Apps is useful but not required

How we hire for this

A practical take-home exercise plus a live technical round, focused on engineering judgment rather than trivia — the differentiator is whether you understand what you built well enough to defend and adapt it live.

Working style

We run like a small, high-output team: you own systems from design through production operation, and you'll ship faster here than anywhere with a platform org between you and prod. The team is in constant contact through the day, and business-critical deliverables sometimes mean flexibility outside standard hours. If you want a clean separation between work and life, this isn't the right fit.