GEN AI Data Scientist
1 day ago
Role: Gen AI Data Scientist
Location: PAN India
Experience: years
Skills: Python, AIML, NLP, Machine Learning, Data Science, Gen AI, AWS, Azure
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.
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