Senior GenAI Engineer

BookMyMentor; Bengaluru; On-site; INR 2,000,000 - 2,500,000; Full time

Job summary

Zenskar is seeking a Senior GenAI Engineer to lead the AI layer of its products, focusing on building reliable features that improve enterprise client operations. You will manage AI production systems, ensuring their usability and trustworthiness while driving meaningful impact.

The ideal candidate has solid engineering experience in AI and possesses strong frontend and backend skills, particularly in React. This role requires a proactive approach to engineering rigor in AI development, making it integral to Zenskar's mission.

Qualifications

• 4–6 years of software development experience with AI features.
• Ability to manage prompt lifecycle and evaluate model performance.
• Responsible AI awareness focusing on third-party model implications.

Responsibilities

• Own the AI layer to build features for enterprise clients.
• Ensure AI features are reliable, observable, and well-evaluated.
• Work with various teams for real user trust and usability.

Skills

AI/LLM-powered features

Latency and cost optimization

Responsible AI and data privacy

Frontend development in React

Strong backend engineering

Education

CS degree or equivalent

Tools

React

Deepgram

Whisper

Job description

As a Senior GenAI Engineer at Zenskar, you will own the AI layer of our product building the features that make Zenskar intelligent. This is not a research role and not a prompt-engineering role. You will build production AI systems that enterprise clients depend on, which means reliability, observability, and rigorous evals matter as much as the AI capability itself. You own the full vertical, the model, the pipeline, and the UI.Build and own CS Copilot a real‑time assistant for customer success teams, spanning STT pipelines, live transcription, and LLM‑powered suggestionsBuild LLM‑powered document understanding features extracting structured, reliable data from unstructured enterprise documentsOwn AI feature UIs end‑to‑end you build the interface, not just the model integration layerDesign and maintain an eval framework define what 'working' means for each AI feature and catch regressions before users doDrive model selection and integration decisions choosing the right provider and approach for each use case, managing latency and costOwn AI platform reliability observability, fallback behaviour, and graceful degradation when models failWork closely with product, customer success, and the full‑stack engineer AI features only matter if they are usable and trusted by real users

THE IMPACT YOU'LL MAKEYou will define what AI means at Zenskar the features you ship will be the most visible and differentiated parts of the productCS Copilot, if done well, changes how enterprise customer success teams operate every single day this is a high‑stakes, high‑visibility surfaceYou will establish the engineering culture around AI reliability at Zenskar — evals, observability, and disciplined iterationYour work will directly accelerate enterprise deals AI features are increasingly a buying criterion for our clientsYou will be the person who brings engineering rigour to a domain where most companies ship demos and call it a feature

Requirements

• Must have
• 4–6 years of total software development experience, with at least 1–2 years actively building and shipping AI/LLM‑powered features in production
• CS degree or equivalent — strong engineering fundamentals; this role requires a builder, not a researcher
• Can describe the hard AI problems they have solved — not which models they used, but what broke, what they learned, and what changed as a result
• LLM pipeline engineering — chaining, tool use, structured outputs, prompt management, handling failure modes in production
• Prompt lifecycle management — treats prompts as versioned, testable artefacts with a system for managing changes in production; not ad‑hoc strings
• RAG fundamentals — chunking strategies, retrieval quality, embedding models, and the judgment to know when RAG is the right answer
• Eval discipline — has designed their own eval sets, knows how to measure AI feature quality and catch regressions when models change
• Latency and cost thinking — knows when to use a smaller model, when to cache, and when streaming matters
• Responsible AI and data privacy awareness — understands the implications of passing enterprise data through third‑party models; designs pipelines with this in mind from day one
• Product‑quality frontend — can own AI feature UIs end‑to‑end in React or equivalent
• Strong backend engineering — can own the full feature without needing a backend engineer to make AI code production‑ready
• Good to have
• Agentic systems — multi‑step agents, tool orchestration (LangGraph, CrewAI, or custom), long‑running workflows
• Voice and multimodal pipelines — STT (Deepgram, Whisper), real‑time audio processing, WebSocket streaming
• Fine‑tuning experience — LoRA/QLoRA on open‑source models; signals understanding of model internals beyond the API surface
• Open‑source model deployment — vLLM, Ollama, running models on own infrastructure
• B2B SaaS / enterprise AI experience — building AI for workflows where correctness and auditability matter
• AI‑assisted development — comfortable building AI tools with AI tools; uses Cursor, Copilot, or similar as a genuine force multiplier
• Experience with financial systems, billing platforms, or fintech applications
• Knowledge of SaaS business models and compliance frameworks
• API design and integration experience
• Prior experience working at a startup
• You have been embarrassed by an AI feature failing in production — and you fixed it systematically, not with a workaround
• You think about evals before you think about model choice — reliability is the product
• You apply the same engineering rigour to probabilistic systems that you would to deterministic ones
• You own AI features end to end — the model, the pipeline, the UI, and the fallback behaviour
• You find it unsatisfying to ship a demo — you want to ship something that holds up under real enterprise usage
• The AI landscape changes weekly and you find that energising, not exhausting — you are genuinely curious and stay ahead of it

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. Talk to Zenskar AI like a colleague. Ask questions, give instructions, get insights, and approve decisions through natural conversation without any technical complexity. The platform 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.