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Generative AI Engineer

8 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

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Work Mode

Remote

Job Type

Full Time

Job Description

Job Title:

Generative AI Engineer📍

Location:

Remote🕒

Experience:

4–8 Years👥

Openings:

15 Positions📝

Employment Type:

Full-Time

Role Overview

We are looking for highly skilled and driven

Generative AI Engineers

with expertise in building intelligent systems using

GPT-4

,

LangChain

,

ChromaDB

, and modern microservices architecture. This is an exciting opportunity to be at the forefront of AI innovation, working remotely on cutting-edge projects integrating

Azure AI services

and

prompt engineering techniques

.

Key Responsibilities

Design, develop, and deploy

Generative AI solutions

using GPT-4, LangChain, and FastAPIBuild and optimize

chatbots

and

prompt workflows

for intelligent conversational interfacesImplement

RAG (Retrieval-Augmented Generation)

pipelines using

ChromaDB

and unstructured dataIntegrate solutions with

Azure OpenAI

and other cloud-based AI servicesDesign and deploy

microservices architecture

for scalable AI applicationsCollaborate with cross-functional teams to turn business requirements into functional AI tools

Required Skills

✅ Strong programming skills in

Python

and backend development using

FastAPI

✅ Proven experience with

GPT-4

,

LangChain

, and

ChromaDB

✅ Deep understanding of

prompt engineering

and

chatbot frameworks

✅ Solid experience in designing and deploying

microservices

✅ Hands-on integration experience with

Azure AI/OpenAI services

Nice To Have

Familiarity with

LangGraph

Knowledge of MLOps tools and CI/CD practicesExperience with Docker, Kubernetes, or cloud deployment pipelines

Ideal Candidate Profile

5+ years of experience in

AI/ML

, with at least

2+ years focused on Generative AI

Previously built

LangChain-based chatbots

using GPT-4 and FastAPIExperience in deploying document-based Q&A systems using

ChromaDB

and

RAG architecture

Strong understanding of scalable architecture and production-ready deployments

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