You will be redirected to djinni.co to apply.
About the role
We are building a fintech portfolio platform (encrypted client-side data, market data integrations, bank statement import) and a growing set of supporting services.
Our current delivery bottleneck is the Agentic Delivery Platform (coding layer): a production system where product managers and engineers drive feature work through AI agents - from clarification and prototyping to multi-agent development, preview environments, quality gates, and PRs.
This is not a chatbot wrapper. It is a stateful agent runtime: orchestration, sandboxed worktrees, Docker preview stacks, streaming UI, model routing, interrupt/resume, and human-in-the-loop controls. When this layer is unreliable, product velocity stalls. Making it production-grade is the priority of this role.
What you will own
Primary (blocker)
- Design and harden the agent delivery runtime (sessions, locks, work-state SSOT, recovery after restarts)
- Evolve the multi-agent pipeline: plan → audit → challenge → execute → test/code review → gates → commit
- Own preview lifecycle: lazy per-session stacks, idle suspend/wake, compile repair, publication gates
- Improve agent protocols: structured tools, prompt policies, read-only vs write modes, iteration loops
- Operate the coding-layer production path: deploy, isolated agent runtime, preview isolation, reliability
- Model routing: presets, per-agent model roster, allowlists, cost/latency/quality trade-offs
Secondary (ecosystem)
- Contribute across a multi-repo stack when needed: main web app (React/GraphQL), Python microservices (LLM parsing, market/FX data), shared libraries
- Keep cross-service contracts stable while agents and humans ship changes safely
Domain
- Portfolio / wealth-tech product surfaces
- Client-side encryption constraints (server never sees plaintext sensitive data)
- Market data and statement-import workflows
- Internal AI-assisted delivery tooling used as the main path to ship product work
Requirements
Must-have
- Strong TypeScript across Node and React
- Solid Docker experience (Compose, networking, volumes, prod vs local)
- Experience building stateful backend systems (sessions, locks, idempotency, restart recovery)
- Hands-on work with LLM/agent systems (tool calling, custom orchestrators, agent CLIs, or similar)
- Comfort designing contracts and SSOT (APIs, SSE/events, persisted state) instead of brittle chat heuristics
- Ability to work across multiple services/repos, not only one application package
Strong plus
- Multi-agent orchestration (planner/executor/reviewer patterns), evals, tool-use reliability
- Git worktrees / sandboxed agent runtimes / preview-in-the-loop workflows
- Python services (FastAPI or similar)
- Production ops: CI/CD, reverse proxies, separate app image vs agent runtime
- Interest in current agentic engineering: model routing, AI-native DX, human-in-the-loop systems, failure taxonomy
Nice to have
- GraphQL + MongoDB
- Applied crypto awareness (RSA/AES, client-side encryption constraints)
- Fintech or market-data experience
- Observability for agent pipelines (structured logs, step telemetry)
What success looks like
- The Agentic Delivery Platform stops being the delivery blocker
- Preview and agent runs are predictable under failure (interrupt, orphan processes, flaky builds)
- PMs/engineers can go from intent → reviewable preview/PR with clear gates and recoverable errors
- Multi-agent workflows improve iteratively without collapsing into prompt spaghetti
About the Role
We are building a fintech portfolio platform (encrypted client-side data, market data integrations, bank statement import) and a growing set of supporting services. Our current delivery bottleneck is the Agentic Delivery Platform (coding layer): a production system where product managers and enginee
