Sr Associate-Data Science

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: 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. 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 & Optimiza