GenAI Engineer

fluid.live; St.Thomas Mount-Pallavaram Cantonment Board; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

fluid.live seeks an experienced GenAI Engineer (4–8 years) to design, develop, deploy, and optimize scalable GenAI solutions across text, vision, and audio domains. You will work on LLM-powered apps, RAG pipelines, embeddings, and multimodal AI, leveraging LangChain, LlamaIndex, and Hugging Face, with cloud deployments on AWS/Azure/GCP.

The role requires strong Python expertise, hands-on with LLMs like GPT, LLaMA, Claude, and a solid grasp of vector databases.

Qualifications

• 4-8 years of experience in AI/ML, Generative AI, NLP, or related engineering roles.
• Strong hands-on experience with Python.
• Experience working with LLMs such as GPT, LLaMA, Claude, or equivalent foundation models.
• Strong understanding of Prompt Engineering and LLM Fine-tuning.
• Hands-on experience developing RAG pipelines and embedding-based retrieval systems.
• Experience with LangChain, Hugging Face, and/or LlamaIndex.
• Experience with vector databases such as FAISS, Pinecone, Weaviate, or equivalent.
• Understanding of embeddings, semantic search, vector similarity, and retrieval techniques.
• Experience developing or integrating multimodal AI solutions involving text, vision, and/or audio.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of API integration, deployment, scalability, and performance optimization.

Responsibilities

• Design and develop Generative AI and LLM-powered applications using models such as GPT, LLaMA, Claude, and other foundation models.
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, including document processing, chunking, embeddings, retrieval, and response generation.
• Implement effective prompt engineering techniques and evaluate LLM outputs for accuracy, relevance, and consistency.
• Work on LLM fine-tuning and model customization based on business and application requirements.
• Develop AI applications using frameworks such as LangChain, LlamaIndex, and Hugging Face.
• Implement and manage vector search and similarity-based retrieval using databases such as FAISS, Pinecone, Weaviate, or equivalent technologies.
• Develop solutions involving multimodal AI, including text, image/vision, and audio-based use cases.
• Integrate LLMs and AI services through APIs and develop scalable AI-powered backend services.
• Deploy GenAI applications on AWS, Azure, GCP, or other cloud platforms.
• Optimize GenAI solutions for performance, scalability, latency, reliability, and cloud cost.
• Evaluate LLM responses and implement techniques to improve model accuracy and overall application performance.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to translate requirements into production-ready AI solutions.
• Follow best practices for AI application development, security, monitoring, and responsible AI.

Skills

Python

LLMs

Prompt engineering

RAG pipelines

Embeddings

LangChain

LlamaIndex

Hugging Face

Vector databases

Multimodal AI

Cloud platforms

API integration

Tools

FAISS

Pinecone

Weaviate

LangChain

LlamaIndex

Hugging Face

Docker

Kubernetes

CI/CD

MLOps

AWS

Azure

GCP

Job description

GenAI Engineer - 4 to 8 Years

Job Description

We are looking for an experienced GenAI Engineer with 4-8 years of experience in AI/ML and Generative AI development. The ideal candidate should have strong hands-on experience working with Large Language Models (LLMs), RAG pipelines, prompt engineering, embeddings, AI frameworks, and vector databases.

The candidate will be responsible for designing, developing, deploying, and optimizing scalable GenAI solutions across text, vision, and audio-based applications.

Key Responsibilities

• Design and develop Generative AI and LLM-powered applications using models such as GPT, LLaMA, Claude, and other foundation models.
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, including document processing, chunking, embeddings, retrieval, and response generation.
• Implement effective prompt engineering techniques and evaluate LLM outputs for accuracy, relevance, and consistency.
• Work on LLM fine-tuning and model customization based on business and application requirements.
• Develop AI applications using frameworks such as LangChain, LlamaIndex, and Hugging Face.
• Implement and manage vector search and similarity-based retrieval using databases such as FAISS, Pinecone, Weaviate, or equivalent technologies.
• Develop solutions involving multimodal AI, including text, image/vision, and audio-based use cases.
• Integrate LLMs and AI services through APIs and develop scalable AI-powered backend services.
• Deploy GenAI applications on AWS, Azure, GCP, or other cloud platforms.
• Optimize GenAI solutions for performance, scalability, latency, reliability, and cloud cost.
• Evaluate LLM responses and implement techniques to improve model accuracy and overall application performance.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to translate requirements into production-ready AI solutions.
• Follow best practices for AI application development, security, monitoring, and responsible AI.

Required Skills

• 4-8 years of experience in AI/ML, Generative AI, NLP, or related engineering roles.
• Strong hands-on experience with Python.
• Experience working with LLMs such as GPT, LLaMA, Claude, or equivalent foundation models.
• Strong understanding of Prompt Engineering and LLM Fine-tuning.
• Hands-on experience developing RAG pipelines and embedding-based retrieval systems.
• Experience with LangChain, Hugging Face, and/or LlamaIndex.
• Experience with vector databases such as FAISS, Pinecone, Weaviate, or equivalent.
• Understanding of embeddings, semantic search, vector similarity, and retrieval techniques.
• Experience developing or integrating multimodal AI solutions involving text, vision, and/or audio.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of API integration, deployment, scalability, and performance optimization.

Good to Have

• Experience with AI Agents / Agentic AI and tool calling.
• Experience with LLM evaluation frameworks and observability.
• Knowledge of ML/NLP concepts and model evaluation techniques.
• Experience with Docker, Kubernetes, CI/CD, and MLOps.
• Experience working with enterprise GenAI applications and production deployments.

Experience

4-8 Years

Core Keywords

GenAI | Generative AI | LLM | GPT | LLaMA | Claude | Prompt Engineering | Fine-Tuning | RAG | Embeddings | LangChain | LlamaIndex | Hugging Face | Vector Database | FAISS | Pinecone | Weaviate | Multimodal AI | Python | Cloud