Description

Key Responsibilities Design and architect scalable AI and data platforms to support analytics, machine learning, and GenAI use cases. Develop and maintain robust data pipelines, ETL/ELT processes, and data integration frameworks. Build and manage data lakes, data warehouses, and real-time streaming solutions. Collaborate with data scientists, ML engineers, and business stakeholders to deploy AI/ML models into production. Define data governance, security, privacy, and compliance standards. Optimize platform performance, scalability, reliability, and cost efficiency. Architect cloud-native solutions using Azure, AWS, or GCP. Implement MLOps and DataOps best practices. Evaluate and integrate emerging AI technologies and tools. Required Skills Strong experience in Data Engineering and Data Architecture. Expertise in Python, SQL, Spark, Databricks, and data processing frameworks. Experience with cloud platforms (Azure, AWS, or GCP). Knowledge of data lakes, data warehouses, and big data technologies. Hands-on experience with Machine Learning, Generative AI, and MLOps. Experience with orchestration tools such as Airflow, Azure Data Factory, or similar. Understanding of APIs, microservices, and containerization technologies (Docker, Kubernetes). Strong knowledge of data governance and security principles. Skills: Network Routing(WAN Technology)~Network Switching(LAN Technology)~Project Management

Key Skills

Education

Bachelor's degree