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

THE IMPACT YOU'LL MAKE :

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

Ideal Candidate :

- Strong Junior GenAI / AI Backend Engineer Profiles

- Mandatory (Experience 1) : Must have 1+ years of full time expeirence in software development using LLMs (OpenAI / Gemini / similar) in projects (internship / full-time / strong personal projects)

- Mandatory (Experience 2) : Must have strong coding skills in Python and hands-on backend development experience (FastAPI / Django preferred)

- Mandatory (Experience 3) : Must have built or contributed to AI/LLM-based applications, such as chatbots, copilots, document processing tools, etc.

- Mandatory (Experience 4) : Must have basic understanding of RAG concepts (embeddings, vector DBs, retrieval)

- Mandatory (Experience 5) : Must have experience building APIs or backend services and integrating with external systems

- Mandatory (Experience 6) : Must have AI/LLM projects clearly mentioned in CV (with what was built, not just tools used)

- Mandatory (Experience 7) : Must have worked with modern development tools (Git, APIs, basic cloud exposure)

- Mandatory (Tech Stack) : Strong in Python + basic AI/LLM ecosystem

- Mandatory (Company) : Product companies / Funded startups (Series A / B / high-growth environments)

- Mandatory (Education) : Tier-1 institutes only (IITs, BITS, IIITs), Can be skipped if from Top notch product companies