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 :

- Backend Development : Proficient in Python for building and maintaining scalable REST APIs, familiarity with integrating AI services.

- AI/ML Orchestration : Strong expertise in Python with a focus on machine learning, large language models (LLMs), AI orchestration.

- Agentic AI Expertise : Experience in building autonomous AI agents using frameworks like OpenAI Functions, or custom orchestration solutions to handle tool use and multi-step reasoning.

- Generative AI Expertise : Generative AI models (text generation, summarization, conversational AI) and applying prompt engineering techniques.

- Data Management : Solid understanding of structured (SQL), graph (CosmosDB), and unstructured (Elasticsearch) databases; ability to design efficient data access patterns for AI workloads.

- RAG Implementation : Proven experience implementing retrieval-augmented generation using Azure AI Search, AWS OpenSearch, or other vector databases.

- Data Pipelines : Hands-on experience building and managing data ingestion and transformation pipelines using Databricks, Azure Data Factory, or equivalent tools.

- Intent Detection & NLU : Skilled in developing or deploying intent detection models and natural language understanding (NLU) components for conversational AI applications.

Preferred Skills :

- Familiarity with cloud platforms such as Azure and AWS.

- Knowledge of additional AI/ML frameworks and tools.

- Knowledge of Agentic AI

- Experience with DevOps practices and CI/CD pipelines.