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 veteran researchers, portfolio managers, and CTOs from renowned hedge funds and asset management firms. We work closely with a $200B+ global asset management firm as a co-build partner — real workflows, real constraints, real users from day one.

This role is for an early-career engineer who codes exceptionally well and wants to grow fast inside that system — working directly with our senior team, shipping to institutional users, and learning how research-grade AI systems are actually built.

⚡ The Technical Challenge

Our platform runs many AI agents collaborating 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. You'll be helping orchestrate intelligent systems under real-world constraints. ? What You'll Work On

Multi-Agent Systems

- Build agent workflows: inter-agent communication, tool delegation, retries, and error recovery
- Implement context and memory components: state persistence, retrieval layers, reasoning traces

Backend & Async Systems

- Build async-first Python/FastAPI services handling concurrent workflows and long-running jobs
- Work with task orchestration, caching (Redis), queues (Celery), and compute pipelines

Data & Evaluation

- Build pipelines transforming complex, heterogeneous financial data into structured outputs
- Help build evaluation harnesses for output quality — golden datasets, regression tests, LLM-as-judge — so agents are measured,



not vibes-checked

Observability

- Instrument tracing, latency profiling, and usage monitoring
- Make AI systems debuggable, inspectable, and auditable at every layer

Frontend (bonus, not core)

- Contribute to React/Next.js interfaces for inspecting workflows, comparing results, and streaming intermediate outputs

⚙️ 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

- 2–4 years building real software (production internships at strong companies count toward this)
- A public GitHub with at least one stellar agentic-AI project you built yourself — an agent harness, orchestration layer, eval framework, or memory system with real engineering behind it: original code (not forks or tutorials), tests, a README that explains your design decisions. This is a hard requirement — we open every repo, we read the code, and it's the first thing we'll ask you to defend live. Link it in your application; applications without it won't be reviewed.
- An outstanding coder — clean, tested, typed code you're proud to defend line by line
- Strong Python; solid CS fundamentals (we notice compilers, systems projects, and competitive programming)
- Hands-on experience with LLM/agentic systems in production, or genuine exposure to quant finance / trading / markets — either is a strong start,



both is rare and we'll move quick
- You use AI coding tools fluently and can explain and defend every line without them — our process tests both, and we value full transparency about how you build
- High ownership, fast iteration, comfortable being the least experienced person in a very senior room

⭐ Strong Signals

- Agent orchestration, eval pipelines, RAG, or memory systems you built yourself — side projects with real engineering count
- Interest in how investors think — markets, backtests, research workflows
- Open-source contributions to agent-infrastructure or ML tooling projects
- A compiler, systems project, or hardware/embedded tinkering habit
- Startup exposure or anything shipped 0→1, at any scale

? Location

Chennai, on-site. We're a small team building fast, in person. Relocation support available.

? Compensation

- ₹15–30 LPA, calibrated to demonstrated level — where you land in the band depends on your take-home and live defense, not your years
- Equity: performance-based grant, formally reviewed at the end of your first year — we'd rather size it to demonstrated impact than guess on day one
- Clear path up: comp and scope are re-benchmarked as you prove out, not renegotiated from scratch

? Our Process

Short intro call → a take-home you'll genuinely enjoy → a live session where you defend your submission, and your GitHub project, with our senior engineers. We move in days, not months.

⚡ Why This Is Different

Most AI startups wrap APIs, optimize prompts, ship demos.

We're building a research engine — with real institutional users, solving high-stakes problems — where systems thinking beats prompt engineering, and where a 2-year engineer who codes brilliantly gets responsibility most companies reserve for year eight.

If you care about building systems that think, not just respond, we should talk.

📌 Software Engineer (Junior/Mid) — Agentic AI (Quant Research Platform) | Chennai (on-site)
🏢 Jnaara
📍 Chennai