Artificial Intelligence Engineer

Zemoso Technologies; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Zemoso Technologies is seeking a Senior GenAI Engineer to lead end-to-end GenAI feature delivery, spanning backend in Python (Django/FastAPI) and frontend in React, with hands-on work on agentic AI workflows. This role is based in Bengaluru, with relocation to Bangalore supported for the right candidate.

You will design RAG pipelines, integrate with APIs, mentor juniors, and collaborate with product teams while ensuring performance, cost and reliability across systems.

Qualifications

• 5–8 years of experience in software engineering.
• Strong Python (Django/FastAPI) and React development.
• Hands-on production GenAI experience with agentic AI and RAG pipelines.
• Experience with vector databases and LLM APIs.

Responsibilities

• Build and deploy GenAI powered features across backend and frontend.
• Design RAG architectures and integrate GenAI capabilities.
• Own GenAI solutions end-to-end including agentic workflows.
• Mentor junior engineers and review code.

Skills

Python

Django

FastAPI

React

GenAI

Agentic AI

RAG pipelines

LLM APIs

API design

Web integration

Tools

Vector databases

Agent frameworks

Cloud-native deployments

Job description

Senior GenAI Engineer (Python + React + Agentic AI)

Job Title - Senior GenAI Engineer

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: 2-3+ years of hands‑on, production‑level experience, including Agentic AI

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:
• 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:
• 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:
• Tool/function calling
• 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).

Mentoring & Collaboration

• 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

• 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
  • 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

• Familiarity with evaluation frameworks for GenAI.
• Exposure to cloud‑native deployments.
• Experience mentoring junior engineers.