Generative AI Application Developer

Role : AIML Backened Developer

Skill : Generative AI, NLP, LLM, AWS, AIML, Gen AI, AWS, Agentic AI, RAG Implementation

Location : Pune, Hyderabad, Chennai, Bangalore

About the Role :

The ideal candidate should have a solid background in backend development, AI / ML orchestration, and data management , with hands-on experience in Generative AI and Agentic AI frameworks . They will be responsible for building REST APIs , managing data pipelines , and implementing AI / ML solutions such as retrieval-augmented generation (RAG) , intent detection models , and autonomous AI agents to deliver intelligent, production-ready solutions.

Key Responsibilities :

• Backend Development : Design, develop, and maintain REST APIs using Python to integrate AI / ML services.
• Agentic AI Development : Design and orchestrate AI agents capable of reasoning, planning, and executing multi-step tasks by integrating LLMs with APIs, tools, and data sources.
• AI / ML Orchestration : Implement and manage machine learning models, large language models (LLMs), Agentic AI workflows and AI orchestration using Python.
• Generative AI Solutions : Using Generative AI models for tasks like text generation, summarization, conversational AI, and content creation.
• Data Management : Work with structured databases (SQL), graph databases (e.G., CosmosDB), and unstructured data stores (e.G., Elasticsearch).
• RAG Implementation : Build retrieval-augmented generation (RAG) pipelines leveraging Azure AI Search, AWS OpenSearch, or other vector databases for contextual responses.
• Data Pipelines : Design and manage robust data ingestion and transformation pipelines to feed AI models.
• Intent Detection & NLU : Develop or integrate intent detection models and natural language understanding (NLU) solutions to enhance conversational AI.
• Prompt Engineering & Optimization : Create and optimize prompts for LLMs to improve response quality and reduce latency.
• AI Integration : Collaborate with frontend and product teams to embed Gen AI features into enterprise applications.

Required Skills :

ability to design efficient data access patterns for AI workloads.