AI Platform​/Agent Infrastructure Engineer

Build the harness our AI-native engineering model runs on — local dev, async execution, evals, behavioral test infrastructure, agent-aware CI/CD, and the observability that doubles as our SOC 2 audit trail.

Ellis is building the unified data layer for private credit fund managers. We ingest, reconcile, and make sense of the financial data that fund CFOs and controllers live inside every day — NAV calculations, LP returns, reconciliation across GP records and fund administrators, SBIC compliance filings — and turn it into a trusted, queryable foundation for every decision the fund makes.

We're a seed-stage company with real customers, real data, and real financial stakes. Design partners and early clients depend on the accuracy of our platform for their fund reporting today. A silent data error costs us a customer permanently. That trust is the moat, and it's what makes this role matter.

We're making a deliberate bet that one AI-native engineer running multiple agents in parallel against a well-built harness outperforms three traditional full-stacks. We have evidence this is true. We also know the critical qualifier: only if the harness is real. Without it, you ship a house of cards.

This role exists to make the harness real.

You are the load-bearing infrastructure hire for our AI-native engineering model. Everything our product engineers ship — every agent-generated diff, every behavioral test that catches a regression, every eval that defines what "passes" — runs on infrastructure you own.

This is not Dev Ops in the traditional sense. It's a new category: AI engineering infrastructure. Your instincts come from platform and Dev Ex backgrounds, but your domain is non-deterministic systems, LLM-app observability, and the specific failure modes that emerge when agents write production code.

Here's what you're walking into: our current local dev environment cannot run a full agent iteration cycle. The background job system that Lang Graph agents require doesn't exist. The eval suite doesn't exist. You're not inheriting a platform — you're building the foundation that makes the whole model work, and you'll be doing it alongside our lead product engineer from day one.

Crucially, this is a day one partnership with the Lead Product Engineer: you build the system and harness, and they ship the features against it — a tight, mutual dependency. You won't be writing day-to-day product features. Instead, you'll build the systems the engineers shipping product depend on.

• Local dev environment — Our importers are manual CLI runs disconnected from S3 events. An agent iterating locally today cannot test against a real ingestion run. Your first deliverable is a local dev environment where an agent's diff can be run end-to-end before it touches staging.
• Async execution infrastructure — An agent orchestration framework's workflows require async execution. We have no background job system today. You'll select and ship it — a lightweight queue and worker setup on our existing AWS/ECS stack — as part of month 1-2.
• First agent workflow — Get a single workflow for an agent orchestration framework running end-to-end against our FastAPI/PostgreSQL stack. Nothing in our agent roadmap ships without this foundation.

• Skills and eval infrastructure — Build the skills system, prompt library, and eval suite that make agent-generated code production-grade. These are the shared tools every product engineer relies on.
• Automated review pipeline — Stand up multiple layers of automated review (Cursor bot, Claude reviewer, independent Claude agents) so that agent-generated diffs face real scrutiny before human eyes touch them.
• B…