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

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 ChallengeOur 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-deterministicOutputs must be reproducible, explainable, and auditableSystems must balance latency, cost, and reasoning qualityThis is not prompt chaining. You'll be helping orchestrate intelligent systems under real-world constraints.🧠 What You'll Work OnMulti-Agent SystemsBuild agent workflows: inter-agent communication, tool delegation, retries, and error recoveryImplement context and memory components: state persistence, retrieval layers, reasoning tracesBackend & Async SystemsBuild async-first Python/FastAPI services handling concurrent workflows and long-running jobsWork with task orchestration, caching (Redis), queues (Celery), and compute pipelinesData & EvaluationBuild pipelines transforming complex, heterogeneous financial data into structured outputsHelp build evaluation harnesses for output quality — golden datasets, regression tests, LLM-as-judge — so agents are measured, not vibes-checkedObservabilityInstrument tracing, latency profiling, and usage monitoringMake AI systems debuggable, inspectable, and auditable at every layerFrontend (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 For2–4 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 lineStrong 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 fastYou 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 buildHigh ownership, fast iteration, comfortable being the least experienced person in a very senior room⭐ Strong SignalsAgent orchestration, eval pipelines, RAG, or memory systems you built yourself — side projects with real engineering countInterest in how investors think — markets, backtests, research workflowsOpen-source contributions to agent-infrastructure or ML tooling projectsA compiler, systems project, or hardware/embedded tinkering habitStartup exposure or anything shipped 0→1, at any scale📍 LocationChennai, 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 yearsEquity: performance-based grant, formally reviewed at the end of your first year — we'd rather size it to demonstrated impact than guess on day oneClear path up: comp and scope are re-benchmarked as you prove out, not renegotiated from scratch📋 Our ProcessShort 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 DifferentMost 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.