Jnaara
Company:
https://www.linkedin.com/company/jnaara
Seniority: Entry level
Industries: Financial Services
Job details:
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 fast
- 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.
Click on Apply to know more.
