Founding Engineer (Full Stack / Systems)

We're not building another AI app.

We're building an AI-native research system that emulates how top investors think — transforming complex data, ideas, and workflows into structured, decision-grade outputs.

This is a systems + infrastructure problem, not a wrapper.

Jnaara is built by a team of veteran researchers, portfolio managers, and CTOs from renowned hedge funds and asset management firms. We're working closely with a $200B+ global asset management firm as a co-build partner — designing for real workflows, real constraints, and real users from day one.

You won't be assembling tools — you'll be defining how an entire research system thinks and operates.

We're building systems where correctness, auditability, and reasoning quality matter — not just UX. • *⚡ The Technical Challenge**

You'll be working on a platform where XXX+ AI agents today (rapidly scaling) collaborate across complex, multi-step workflows — each with different tools, data access patterns, and reasoning strategies. • Workflows are long-running, stateful, and non-deterministic • Outputs must be reproducible, explainable, and auditable • Systems must balance latency, cost, and reasoning quality

This is not prompt chaining. This is orchestrating intelligent systems under real-world constraints. • *🧠 What You'll Build**

𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 • Design systems coordinating interacting agents across dependency graphs, retries, and evaluation loops • Build abstractions for workflows (not chat chains) — inter-agent communication, tool delegation, and error recovery • Implement context and memory systems: state persistence, retrieval layers, and reasoning traces

𝗗𝗮𝘁𝗮 & 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Architect scalable pipelines that transform complex, heterogeneous data into structured outputs • Design flexible data access layers for dynamic, agent-driven analysis • Enable large-scale experimentation with reproducibility and performance in mind

𝗕𝗮𝗰𝗸𝗲𝗻𝗱 & 𝗔𝘀𝘆𝗻𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Build async-first backend services (Python / FastAPI) handling concurrent workflows, long-running jobs, and high-throughput processing • Design task orchestration, caching (Redis), queuing (Celery), and compute pipelines • Architect for bursty workloads and hybrid compute (batch + real-time)

𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Implement tracing, latency profiling, and usage monitoring • Build evaluation pipelines for output quality and system performance • Make AI systems debuggable, inspectable, and auditable at every layer

𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 • Build real-time, data-rich interfaces (React / Next.js) for interacting with complex workflows • Design UX for inspecting intermediate outputs, comparing results, and configuring systems • Stream intermediate results (WebSockets / SSE) as workflows execute • Own the design system and component architecture

𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 • Own cloud infrastructure (AWS) — compute, storage, networking, and security • Build CI/CD pipelines, automated testing, and deployment workflows • Implement infrastructure-as-code for reproducible environments • Design for data governance: encryption, RBAC, audit logging • *⚙️ Tech Stack (Current Direction)**

Backend: Python, FastAPI, Celery, Redis

Frontend: React, Next.js, TypeScript

Data: Snowflake, Postgres, S3

AI Layer: Multi-agent orchestration, retrieval systems, LLM APIs

Infra: AWS, Terraform, GitHub Actions • *🧩 What We're Looking For** • 6–8 years building production-grade systems • Strong in Python (APIs, async systems, data workflows) and React / Next.js • Thinks in systems, not endpoints • Comfortable across backend, data, and frontend layers • Has built something from 0 → 1 • Hands-on with cloud infrastructure and modern DevOps • Strong data instincts (SQL, modeling, performance) • High ownership, fast iteration mindset • *⭐ Strong Signals (Not Required)** • Experience working with LLMs or AI systems in production • Familiarity with data pipelines or async job orchestration • Real-time systems or event-driven architecture experience • Startup or founding engineer experience • Interest in complex decision-making systems or research workflows • *🧠 Technical Problems You'll Tackle** • Orchestrating non-trivial multi-agent systems with real interdependencies • Designing memory and context layers for reasoning systems • Balancing latency vs cost vs quality in AI workflows • Making outputs traceable, reproducibl