Data Architect

As an experienced data architect, you will be responsible for designing and implementing enterprise-grade data architectures to support GenAI, autonomous agents, and real-time inference. Your role will involve the following key responsibilities: - Designing data platforms for multi-agent systems, focusing on orchestration, communication, and memory management. - Architecting end-to-end pipelines for AI/ML workloads, including ingestion, feature engineering, and model lifecycle management. - Implementing and scaling retrieval-augmented generation (RAG), fine-tuning frameworks, and knowledge graphs in production. - Developing backend systems for agentic workflows such as task decomposition, context switching, and reinforcement feedback loops. - Leading data modeling, governance, metadata management, and master data management initiatives. You should possess the following qualifications and skills to excel in this role: - 5+ years of experience in data architecture or related roles. - Hands-on experience with AI/ML frameworks including TensorFlow, PyTorch, and SciKit-learn. - Proven experience with Generative AI models and LLMs such as GPT, Claude, or LLaMA. - Proficiency with orchestration frameworks like LangChain, LlamaIndex, or Langgraph. - Exposure to multi-agent frameworks including CrewAI, AutoGen, ReAct, or CAMEL. - Knowledge of agentic AI design principles and feedback loop development. - Understanding of data governance, security, and compliance frameworks like GDPR or HIPAA. - Any Graduate degree in Computer Science, Data Science, Engineering, or a related field. Preferred skills for this role include: - Experience building production-grade agentic systems with adaptive learning. - Familiarity with knowledge graphs, semantic search, and advanced RAG pipelines. - Cloud or AI/ML certifications such as AWS Certified Data Analytics or Google ML Engineer. As an experienced data architect, you will be responsible for designing and implementing enterprise-grade data architectures to support GenAI, autonomous agents, and real-time inference. Your role will involve the following key responsibilities: - Designing data platforms for multi-agent systems, focusing on orchestration, communication, and memory management. - Architecting end-to-end pipelines for AI/ML workloads, including ingestion, feature engineering, and model lifecycle management. - Implementing and scaling retrieval-augmented generation (RAG), fine-tuning frameworks, and knowledge graphs in production. - Developing backend systems for agentic workflows such as task decomposition, context switching, and reinforcement feedback loops. - Leading data modeling, governance, metadata management, and master data management initiatives. You should possess the following qualifications and skills to excel in this role: - 5+ years of experience in data architecture or related roles. - Hands-on experience with AI/ML frameworks including TensorFlow, PyTorch, and SciKit-learn. - Proven experience with Generative AI models and LLMs such as GPT, Claude, or LLaMA. - Proficiency with orchestration frameworks like LangChain, LlamaIndex, or Langgraph. - Exposure to multi-agent frameworks including CrewAI, AutoGen, ReAct, or CAMEL. - Knowledge of agentic AI design principles and feedback loop development. - Understanding of data governance, security, and compliance frameworks like GDPR or HIPAA. - Any Graduate degree in Computer Science, Data Science, Engineering, or a related field. Preferred skills for this role include: - Experience building production-grade agentic systems with adaptive learning. - Familiarity with knowledge graphs, semantic search, and advanced RAG pipelines. - Cloud or AI/ML certifications such as AWS Certified Data Analytics or Google ML Engineer.