Junior GenAI Engineer

BookMyMentor; Bengaluru; On-site; INR 600,000 - 800,000; Full time

Job summary

Zenskar is seeking a Junior GenAI Engineer to contribute to AI-powered features within their product. You will work closely with senior engineers to build reliable production AI systems with a strong focus on customer success.

Responsibilities include developing LLM features, ensuring AI reliability, and participating in model selection. Ideal candidates bring a CS degree, software development experience, and a genuine curiosity about AI.

Qualifications

• 1–3 years of software development experience with some exposure to AI/LLM concepts.
• Solid engineering fundamentals; eagerness to learn AI pipeline engineering.
• Strong English communication skills and the ability to handle ambiguous requirements.

Responsibilities

• Contribute to AI layer of product and build production AI systems.
• Work on LLM-powered features and document understanding.
• Assist with AI platform reliability and integration decisions.

Skills

Software development experience

AI/LLM concepts

Strong English communication skills

Frontend awareness in React

Backend engineering foundation

Education

CS degree or equivalent

Job description

Job Overview

As a Junior GenAI Engineer at Zenskar, you will contribute to the AI layer of our product – working on features that make Zenskar intelligent. This is not a research role and not a prompt‑engineering role. You will learn to build production AI systems that enterprise clients depend on, under the guidance of senior engineers. Reliability, observability, and rigorous evals matter as much as the AI capability itself, and you will grow into full ownership over time.

Contribute to CS Copilot – a real‑time assistant for customer success teams, spanning STT pipelines, live transcription, and LLM‑powered suggestions. Work on LLM‑powered document understanding features – extracting structured, reliable data from unstructured enterprise documents. Help build AI feature UIs – learning to own the interface layer, not just the model integration. Contribute to eval frameworks – learning to define what “working” means for AI features and help catch regressions. Support model selection and integration decisions – learning to choose the right provider and approach for each use case. Assist with AI platform reliability – observability, fallback behaviour, and graceful degradation when models fail. Work closely with product, customer success, and the full‑stack engineer – learning how AI features become usable and trusted by real users.

Impact

You will grow into defining what AI means at Zenskar – contributing to features that are the most visible and differentiated parts of the product. CS Copilot, if done well, changes how enterprise customer success teams operate every single day – you’ll be part of that journey. You will learn engineering culture around AI reliability – evals, observability, and disciplined iteration. Your work will contribute to enterprise deals – AI features are increasingly a buying criterion for our clients. You will grow from shipping supported features to shipping things that hold up under real enterprise usage.

Qualifications

• 1–3 years of total software development experience, with some exposure to AI/LLM concepts or tooling (personal projects, coursework, or professional experience welcome)
• CS degree or equivalent – solid engineering fundamentals; this role requires a builder who wants to grow
• Genuine curiosity about AI and eagerness to learn – you follow the space, experiment with models, and want to go deep
• Basic understanding of LLMs – knows how prompting works, has used APIs like OpenAI or Anthropic, understands basic concepts like context windows and tool use
• Eagerness to learn LLM pipeline engineering – chaining, structured outputs, handling failure modes; willing to grow into production ownership
• Interest in RAG and retrieval – curious about how to ground AI outputs in real data
• Understanding that AI features need evals – appreciates that “it works for me” is not a deployment bar
• Product‑quality frontend awareness – can contribute to AI feature UIs in React or equivalent
• Backend engineering foundation – can write APIs and services, wants to grow into owning the full feature stack
• Strong English communication skills – can work with ambiguous requirements and ask good questions

Good to have

• Any hands‑on LLM project experience – personal, open‑source, or professional
• Familiarity with agentic concepts – multi‑step workflows, tool use, orchestration frameworks
• Familiarity with open‑source models – Ollama, vLLM, or local model experimentation
• B2B SaaS / enterprise software exposure – understands that correctness and auditability matter in enterprise contexts
• AI‑assisted development – already uses Cursor, Copilot, or similar tools to build faster
• Interest in financial systems, billing platforms, or fintech applications
• Knowledge of SaaS business models and compliance frameworks
• Prior experience working at a startup

What Drives You

• You are embarrassed when something you built doesn’t work as expected – and you want to understand why, not just patch it
• You think reliability matters as much as capability – you want to ship AI that holds up, not just demos that impress
• You apply engineering rigour even to probabilistic systems – you want to measure things, not just eyeball them
• You want to own AI features end‑to‑end someday – the model, the pipeline, the UI, and the fallback behaviour
• You find it unsatisfying to ship a demo – you want to ship something real users actually trust
• The AI landscape changes weekly and you find that energising, not exhausting – you stay curious and keep learning

About the Company

Zenskar is an AI‑Native Order‑to‑Cash platform that automates billing, revenue recognition, collections, and SaaS metrics for modern finance teams. It offers unlimited pricing flexibility to set up subscriptions, usage‑based billing, or custom contracts without requiring developers. With 200+ plug‑and‑play integrations and comprehensive migration support, companies can implement Zenskar in weeks rather than months. Let your finance teams collaborate with AI instead of drowning in spreadsheets or waiting for developers, increasing efficiency without expanding headcount.