Description :

Role : AI Engineer LLM

Location : Bangalore / Mumbai / Work From Home

Experience : 4 to 6 Years

Function : Data Science & Analysis ? Data Science / Machine Learning

Tech Stack : Machine Learning, Generative AI, LLMs, Python, NLP, LangChain, LangGraph, RAG

About the Role :

We are looking for a highly motivated AI Engineer LLM to design, build, and optimize intelligent AI-powered systems leveraging Large Language Models (LLMs) and Generative AI technologies.

The ideal candidate will have hands-on experience in building conversational AI solutions, fine-tuning models, implementing Retrieval-Augmented Generation (RAG) pipelines, and creating scalable AI workflows.

In this role, you will work closely with cross-functional teams to craft personalized and engaging conversational experiences while driving innovation in AI systems from the ground up.

Key Responsibilities :

- Collaborate closely with product, engineering, and design teams to build intuitive and intelligent conversational experiences for users.

- Design, develop, and deploy LLM-powered applications and conversational AI systems tailored to business and customer requirements.

- Build and optimize chatbots, AI assistants, and conversational workflows using Generative AI technologies.

- Fine-tune Large Language Models (LLMs) with domain-specific and proprietary datasets to improve performance, relevance, and personalization.

- Design and implement Retrieval-Augmented Generation (RAG) frameworks to enrich LLM responses with external knowledge sources and real-time contextual data.

- Experiment with and implement different RAG architectures, embeddings, vector databases, and retrieval mechanisms to optimize model output quality.

- Analyze available datasets to identify opportunities for creating personalized user experiences and intelligent recommendations.

- Develop scalable AI pipelines and workflows using frameworks such as LangChain and LangGraph.

- Build and optimize prompt engineering strategies to improve LLM accuracy, reliability, and efficiency.

- Work independently to deliver assigned features while taking ownership of outcomes and problem-solving in a fast-paced startup environment.

- Set up and implement new AI workflows, systems, tools, and best practices from scratch while collaborating with internal teams.

- Continuously evaluate and integrate advancements in LLMs, NLP, Machine Learning, and Generative AI to enhance product capabilities.

- Ensure AI systems meet standards for performance, scalability, security, and reliability.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related fields.

- 4 to 6 years of experience in Machine Learning, NLP, Data Science, or AI Engineering.

- Strong hands-on experience working with Large Language Models (LLMs) and Generative AI frameworks.

- Proven experience in building conversational bots/chatbots and AI-powered assistants.

- Hands-on expertise in fine-tuning models and optimizing performance using domain-specific data.

- Strong experience implementing RAG (Retrieval-Augmented Generation) architectures and retrieval systems.

- Proficiency in Python programming and experience with machine learning frameworks and NLP libraries.

- Experience with LangChain, LangGraph, prompt engineering, and AI orchestration frameworks.

- Strong understanding of Natural Language Processing (NLP) concepts, embeddings, vector databases, and semantic search.

- Ability to work independently, take ownership, and thrive in a startup environment.

- Strong analytical, problem-solving, and communication skills.

Preferred Qualifications :

- Experience working with financial services, investing, or fintech-related domains would be an added advantage.

- Familiarity with cloud platforms (AWS, Azure, or GCP) and scalable AI deployment architectures.

- Knowledge of MLOps practices, model deployment, and monitoring frameworks.

- Experience in building production-grade AI applications and integrating APIs into enterprise systems.