AI Engineer | Immediate | Hybrid

Bandhan Technologies Private Limited

Bengaluru

Hybrid

INR 4,500,000 - 7,500,000

Full time

8 days ago
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Job summary

Bandhan Technologies Private Limited in Bengaluru seeks a Senior AI Engineer with strong GenAI skills to build live business applications, connecting AI features to existing software, databases, and daily workflows.

You will implement a smart catalogue search, AI chat with automated actions, and enterprise integrations (ERP like NetSuite) while advancing vector search, LangChain stacks, and model API orchestration.

Qualifications

  • 5 to 7 years of total software development experience.
  • Hands-on with GenAI, LLMs, and production AI engineering.
  • Proficiency in Python backend development.
  • Experience with FastAPI, Flask, AsyncIO.
  • Vector databases & search experience.
  • LangChain, LlamaIndex, LangGraph, or CrewAI multi-step reasoning.
  • Direct integration with major model APIs (OpenAI, Anthropic, Google Gemini, HF).
  • Enterprise integration with ERP systems (NetSuite, SAP).
  • Collaborative Filtering / Hybrid ML vs LLM-based reranking.

Responsibilities

  • Smart Catalogue Search & Recommendations: build a search and recommendation engine.
  • Interactive AI Chat & Automation: create an AI chat interface with automated actions.
  • Design an Actionable Agentic Chat Interface for enterprise workflows.
  • Call Transcript Summarization: pipelines to summarize long transcripts.
  • Integration & Backend Work: connect AI tools to ERP and backend systems.
  • Build Intelligent Recommendation Engine.

Skills

GenAI & LLMs
Python backend
FastAPI
REST APIs
Microservices
Vector databases
LangChain
LlamaIndex
LangGraph
CrewAI
OpenAI API
Anthropic API
Google Gemini
Hugging Face
ERP integration
NetSuite
SAP
Model orchestration

Tools

NetSuite
SAP
Flask
AsyncIO
LangChain
LlamaIndex
LangGraph
CrewAI
OpenAI API
Anthropic API
Google Gemini API
Hugging Face

Job description

Role Summary

We Are Looking For a experienced Senior AI Engineer who is strong in Generative AI skills to build real, working business applications. This is not just tweaking prompts, rather connecting smart AI features into client's existing software, databases, and daily workflows.

Key Responsibilities
  • Smart Catalogue Search & Recommendations: Build a search and recommendation engine that can navigate millions of product variations (like 50,000+ types of office furniture) and help users find exact, compliant items quickly.
  • Interactive AI Chat & Automation: Create an AI chat interface that doesn't just answer questions, but takes actual actionslike creating Purchase Orders (POs), triggering compliance checks, and updating core backend systems.
  • Design an Actionable Agentic Chat Interface capable of initiating enterprise workflows
  • Call Transcript Summarization: Build pipelines to process, clean, and summarize long customer call transcripts so key details are captured automatically.
  • Integration & Backend Work: Connect these AI tools into existing company platforms (including ERP systems like NetSuite) so everything runs seamlessly behind the scenes.
  • Build Intelligent Recommendation Engine
Must-Have Experience
  • 5 to 7 years of total software development experience, with at least 23+ years directly hands-on with GenAI, Large Language Models (LLMs), and production AI engineering.
  • Proficiency in Python: Advanced mastery of core backend development (FastAPI, Flask, AsyncIO), REST APIs, and microservices architecture.
  • Vector Databases & Search: Proven experience with vector engines/DB’s.
  • Agentic Frameworks & LLM Stack: Hands-on experience with LangChain, LlamaIndex, LangGraph, or CrewAI for multi-step reasoning, memory retention, and tool calling.
  • Model APIs & Orchestration: Direct integration experience with major foundation model APIs (OpenAI, Anthropic, Google Gemini, Hugging Face).
  • Enterprise Integration: Experience connecting AI services to ERP systems (NetSuite, SAP), custom backends, and external third-party software.
  • Collaborative Filtering / Hybrid ML vs. LLM-based Reranking
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