Job Description
The Data Scientist will be responsible for designing, developing, and deploying end-to-end data pipelines, AI solutions, and large language models (LLMs) across various applications. This role requires a solid foundation in data engineering, AI ethics, cloud architecture, and effective communication skills for translating technical information to stakeholders. Our ideal candidate is proficient in Azure environments and has hands-on experience with generative AI, multi-agent frameworks, and various AI development tools.
Responsibilities
- Data Engineering & Pipeline Development:
- Develop and maintain scalable data engineering pipelines that handle structured and unstructured data efficiently.
- Integrate data from various sources and ensure the integrity, quality, and security of data throughout its lifecycle.
- Optimize data processing workflows and implement solutions for handling large volumes of data.
- AI Architecture & Cloud Environment (Azure):
- Design and architect AI and ML models on Azure, focusing on scalable, high-performance solutions.
- Leverage Azure tools and services, such as Azure AI, Azure AI Search, and Azure Document Intelligence, to deploy and manage AI models.
- Collaborate with engineering and DevOps teams to ensure the efficient deployment of AI solutions in cloud environments.
- LLM Deployment for Specific Applications:
- Deploy and fine-tune large language models (LLMs) to meet specific client needs.
- Implement generative AI models, including Retrieval-Augmented Generation (RAG), and adapt them to provide insightful, context-specific outputs.
- Research and implement various multi-agent frameworks for handling complex, multi-step AI interactions.
- AI Ethics, Responsible AI, and Bias Mitigation:
- Ensure all AI models adhere to responsible AI practices and mitigate bias across different applications.
- Stay updated with AI ethics, compliance regulations, and best practices in developing fair, transparent AI models.
- Provide guidance on implementing responsible AI and bias mitigation techniques for specific client applications.
- Stakeholder Communication and Requirement Gathering:
- Collaborate with stakeholders to understand project requirements and translate business needs into actionable AI solutions.
- Present complex technical concepts to non-technical stakeholders and ensure clear, effective communication across teams.
- Engage in requirements gathering to develop tailored solutions aligned with client expectations and project objectives.
- Technology Stack & Tools:
- Programming: Advanced proficiency in Python for AI model development and data engineering.
- Frameworks: Experience with FastAPI or equivalent frameworks for API development and deployment.
- Generative AI Tools: Proficient in OpenAI and other generative AI tools for model development.
- Single and Multi-Agent Frameworks: Knowledge of frameworks for handling single and multi-agent AI interactions.
- Azure AI Search & Document Intelligence: Experience with Azure AI Search, Azure Document Intelligence, or equivalent tools for information retrieval and document processing.
Required Skills & Qualifications
- Bachelor's or master's degree in computer science, Data Science, AI, or a related field.
- Proven experience in data engineering and pipeline development, with a focus on deploying AI models in cloud environments.
- Strong understanding of Microsoft Azure cloud architecture and tools for AI and ML deployment.
- Proficiency in Python and experience with frameworks like FastAPI or similar for API development.
- Experience with LLMs, generative AI techniques (including RAG), and OpenAI API for specific applications.
- Familiarity with single and multi-agent AI frameworks for managing complex interactions.
- Commitment to responsible AI practices, ethics, and bias mitigation in AI models.
- Excellent communication skills with the ability to convey technical insights to non-technical stakeholders.
