Engineering

- Mid-Senior

Python / LLM Engineer Own the AI layer of a live coaching platform: an LLM assistant grounded in a real behavioural-science methodology, retrieval over Qdrant, and the evaluation discipline that keeps it honest.

Jaipur, India

- remote friendly (IST) Full-timeBispy bot, Qdrant retrieval, AI coaching, scoring/ML

All roles are based in Jaipur, India — on-site only, not remote. Apply for this role All open roles The role

You'll own the AI capability of a production platform: Bispy, an LLM coaching assistant that must stay inside a client's behavioural-science methodology — an LLM freelancing generic advice would undermine the intellectual property the product is built on. The work is the unglamorous, decisive part of applied AI: retrieval quality over Qdrant, prompt and context engineering, evaluation suites that catch regressions before users do, and the scoring/ML pipeline behind assessments.

Role details

What you'll do

- Own the LLM coaching layer end to end: prompts, context assembly, retrieval, fallbacks, and cost control
- Build and tune retrieval over Qdrant: chunking, embeddings, hybrid search, and relevance you can measure
- Maintain evaluation as a first-class system — golden datasets, automated scoring, regression gates on every change
- Develop the assessment scoring/ML pipeline in Python, with results that are explainable to non-engineers
- Keep data boundaries structural: sensitive assessment data gets the minimum-necessary treatment, always

What you bring
- 3+ years of production Python, with 1+ year building LLM-backed features that shipped
- Hands-on retrieval/RAG experience — vector stores (Qdrant or similar), embeddings, and the failure modes of both
- You treat evaluation as engineering, not vibes: you can describe how you'd catch a quality regression automatically
- Comfort with the full API landscape (Claude,



GPT, Gemini, open-weights) and choosing empirically
- Robust written communication — model behavior gets documented, not remembered

Nice to have
- Classical ML fundamentals (scikit-learn-level) for scoring models beyond the LLM
- Experience constraining LLMs to a domain methodology or brand voice with measurable adherence
- Voice or real-time AI exposure

You'll work with

PythonLLM APIsQdrantRAGEvalsPostgreSQL

What you get The deal, plainly

Senior-led delivery: your work is reviewed by people who've shipped production systems, and you see how they think An AI-native workflow — modern AI tooling is standard practice here, with human review as the quality bar

Real products in production: client work that ships weekly and gets used, not internal demos

Async-first, writing-heavy culture on IST, with deliberate US/EU overlap instead of late-night calls

Competitive compensation, reviewed on outcomes

How we hire

Four steps, no gauntlet

- 01

Apply Send the form on the role page — the cover note and links matter far more than a perfectly formatted CV.
- 02

Intro call (30 min) A real conversation about the role, your work, and whether the fit is mutual. No trick questions.
- 03

Practical round A scoped, role-relevant exercise or portfolio deep-dive — designed to respect your time, not a weekend of free work.
- 04

Founder conversation & offer You meet the person you'll actually work with. If it's a yes on both sides, we move fast.

Apply

Apply — Python / LLM Engineer (Mid-Senior)

The cover note and links matter far more than a perfectly formatted CV. We read every application.

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📌 Python / LLM EngineerMid-Senior (Jaipur)
🏢 Narix Labs
📍 Jaipur