Senior GenAI Engineer

Zemoso Technologies; Bengaluru; On-site; INR 2,800,000 - 4,600,000; Full time

Job summary

Zemoso Technologies in Bangalore is seeking a Senior GenAI Engineer with 5–8 years of overall experience and 3–4+ years in GenAI, including agentic AI. You will own GenAI features spanning backend, frontend, and AI layers, design RAG architectures, and mentor junior engineers in a fast-paced environment.

The role emphasizes hands-on delivery of GenAI projects, performance, cost awareness, and collaboration with product and data teams. Relocation to Bangalore or local candidates are welcome.

Qualifications

• Strong Python/Django and React experience.
• Hands-on GenAI in production with agentic AI.
• Experience building end-to-end GenAI features across backend and frontend.

Responsibilities

• Build and deploy GenAI-powered features across backend (Python - Django/FastAPI) and frontend (React) layers.
• Design and implement RAG pipelines and retrieval strategies.
• Own GenAI solutions end-to-end from design to production rollout.

Skills

Python

Django/FastAPI

React/Svelte

GenAI in production

Agentic AI

RAG pipelines

LLM APIs

API design

Performance & cost

Tools

Vector databases

Embeddings

APIs integration

Job description

Location

Bangalore, or candidates ready to relocate to Bangalore.

Role Overview

We are looking for a Senior GenAI Engineer with strong core engineering foundations and hands‑on experience building and deploying GenAI solutions. This role is execution‑ and ownership‑focused - responsible for delivering GenAI features end‑to‑end (backend + frontend), working with agentic AI workflows, and mentoring junior engineers while handling ambiguous requirements. This position is part of our GenAI hiring phase, where we prioritize strong full‑stack/backend engineers who have recently built and shipped GenAI and agentic AI solutions in production.

Experience Profile

• Overall experience: 5-8 years
• Core engineering background: Python (Django / FastAPI) + React
• GenAI experience: 3-4+ years of hands‑on, production‑level experience, including Agentic AI
• Core stack: Python/Django, with React/Svelte on the front end.
• GCP/Azure is the primary cloud platform , it's nice to have.
• Location: Bangalore (or willing to relocate to Bangalore)

Key Responsibilities

GenAI Solution Development

• Build and deploy GenAI‑powered features within real‑world products, spanning backend (Python - Django/FastAPI) and frontend (React) layers.
• Design and implement RAG pipelines, including Embeddings, Vector databases, and Retrieval strategies.
• Integrate GenAI capabilities with backend systems via APIs and services.
• Build responsive, GenAI‑integrated UI/UX components in React to surface AI‑driven features to end users.

System & Solution Ownership

• Own GenAI solutions end‑to‑end, from design to production rollout across backend, frontend, and AI layers.
• Design RAG architectures and decide chunking strategies and retrieval approaches.
• Handle ambiguous requirements and convert them into working, shippable solutions.

Reliability, Quality & Performance

• Address latency, cost, and evaluation considerations in GenAI systems.
• Implement guardrails and safety mechanisms.
• Contribute to monitoring and basic operational readiness.

Agentic AI (Core Focus)

• Build and own agent-based workflows, including multi‑step reasoning flows, tool/function calling, and orchestrated GenAI interactions.
• Work hands‑on with agent frameworks to design and deploy production‑grade agentic systems (deep research not required, but practical build experience expected).
• Mentor junior engineers working on GenAI components.
• Review GenAI‑related code and designs, across both backend and frontend implementations.
• Collaborate closely with backend, frontend, data, and product teams.

Must‑Have Skills

• Core stack: Python/Django, with React/Svelte on the front end.
• GCP/Azure is the primary cloud platform , it's nice to have.
• Strong engineering experience in Python (Django / FastAPI) and React.
• Hands‑on experience building GenAI applications in production.
• Experience with RAG pipelines, Vector databases, LLM APIs, and Agentic AI (multi‑step reasoning, tool/function calling, orchestration).
• Solid understanding of API design and backend integrations.
• Ability to reason about trade‑offs in performance, cost, and reliability.

Nice‑to‑Have Skills

• Azure is good to have, as some requirements may involve Azure depending on the project.
• Familiarity with evaluation frameworks for GenAI.