Data and AI Architect

As a Data Architect at our company, you will play a crucial role in designing and implementing scalable data architectures, ensuring data governance and compliance, and enabling advanced analytics and AI use cases. Your responsibilities will include: - Designing and implementing scalable, secure, and high-performance data architectures such as data lakes, lakehouses, and warehouses. - Defining data modelling standards like dimensional, data vault, and domain-driven design. - Architecting batch and real-time data pipelines utilizing technologies like Apache Spark and Apache Kafka. - Ensuring data governance, quality, lineage, and compliance across systems. - Building and optimizing ETL/ELT workflows using orchestration tools like Apache Airflow. - Developing scalable ingestion pipelines for different types of data. - Implementing CI/CD pipelines and infrastructure automation with tools like Terraform. - Containerizing and deploying services using Docker and orchestrating via Kubernetes. - Designing and operationalizing ML pipelines, including training, evaluation, and deployment workflows. - Building and managing feature stores and model lifecycle pipelines. - Integrating ML systems with production-grade data platforms. In the realm of Generative AI & LLM Systems, you will be involved in: - Architecting AI solutions using OpenAI APIs and open-source models like LLaMA. - Building RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge systems. - Developing agentic workflows using frameworks such as LangChain and LangGraph. - Designing and deploying AI agents capable of multi-step reasoning and tool integration. - Implementing Model Context Protocol (MCP)-based architectures for tool interoperability. - Architecting solutions on cloud platforms (AWS, Azure, or GCP). - Designing cost-efficient, scalable storage and compute layers. - Enabling real-time analytics and streaming data use cases. - Collaborating with stakeholders, data scientists, and engineering teams to define data strategy. - Establishing best practices for data architecture, security, and AI governance. - Mentoring engineers and guiding technical decision-making. Qualifications required for this role include: - Bachelors/Masters degree in Computer Science, Engineering, or related field. - Overall 12+ years of experience with 8+ years in data engineering and 2-3+ years in data architecture roles. - Strong hands-on experience with distributed data processing (Spark), workflow orchestration (Airflow), and designing ML pipelines. - Solid understanding of data modeling, data warehousing, big data ecosystems, containerization, and orchestration. - Proficiency in Python, SQL, and/or Scala. Preferred qualifications and nice-to-have skills are also outlined in the job description. As a Data Architect at our company, you will play a crucial role in designing and implementing scalable data architectures, ensuring data governance and compliance, and enabling advanced analytics and AI use cases. Your responsibilities will include: - Designing and implementing scalable, secure, and high-performance data architectures such as data lakes, lakehouses, and warehouses. - Defining data modelling standards like dimensional, data vault, and domain-driven design. - Architecting batch and real-time data pipelines utilizing technologies like Apache Spark and Apache Kafka. - Ensuring data governance, quality, lineage, and compliance across systems. - Building and optimizing ETL/ELT workflows using orchestration tools like Apache Airflow. - Developing scalable ingestion pipelines for different types of data. - Implementing CI/CD pipelines and infrastructure automation with tools like Terraform. - Containerizing and deploying services using Docker and orchestrating via Kubernetes. - Designing and operationalizing ML pipelines, including training, evaluation, and deployment workflows. - Building and managing feature stores and model lifecycle pipelines. - Integrating ML systems with production-grade data platforms. In the realm of Generative AI & LLM Systems, you will be involved in: - Architecting AI solutions using OpenAI APIs and open-source models like LLaMA. - Building RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge systems. - Developing agentic workflows using frameworks such as LangChain and LangGraph. - Designing and deploying AI agents capable of multi-step reasoning and tool integration. - Implementing Model Context Protocol (MCP)-based architectures for tool interoperability. - Architecting solutions on cloud platforms (AWS, Azure, or GCP). - Designing cost-efficient, scalable storage and compute layers. - Enabling real-time analytics and streaming data use cases. - Collaborating with stakeholders, data scientists, and engineering teams to define data strategy. - Establishing best practices for data architecture, security, and AI governance. - Mentoring engineers and guiding technical decision-making. Qualificati