Full-Stack AI Architect

As a Full-Stack AI Architect, you will play a crucial role in designing, building, and scaling end-to-end AI-driven applications. Your responsibilities will span across software architecture, full-stack engineering, and applied AI/ML system design. You will be expected to translate business problems into scalable AI solutions, own the technical blueprint encompassing frontend, backend, data, and AI layers, and lead teams through implementation and productionization. - Key Responsibilities: - Design end-to-end AI system architectures covering data ingestion, model training/inference, APIs, UI, and deployment. - Select appropriate ML/LLM architectures based on use cases and define model lifecycle management. - Architect scalable, secure, and cost-efficient AI platforms. - Design and develop high-performance backend services using Python/Java/Node.js. - Build REST/GraphQL APIs for AI services and enterprise integrations. - Implement authentication, authorization, observability, and fault tolerance. - Optimize latency and throughput for real-time AI inference. - Guide development of AI-enabled user interfaces using React, Angular, or Vue. - Enable conversational, analytical, or decision-support experiences. - Ensure explainability and usability of AI outputs for end users. - Design data pipelines using SQL/NoSQL, data lakes, and warehouses. - Implement MLOps pipelines for model lifecycle management. - Ensure data quality, governance, and compliance. - Required Skills & Qualifications: - Strong experience in Python and one backend language (Java/Node.js). - Expertise in ML/DL frameworks such as TensorFlow, PyTorch, and scikit-learn. - Hands-on experience with LLMs, GenAI, RAG, embeddings, and vector databases. - Solid knowledge of frontend frameworks, with React preferred. - Strong API design and distributed systems knowledge. - Technical Leadership: - Act as a technical authority across AI and full-stack engineering. - Review designs, mentor engineers, and set coding/architecture standards. - Collaborate with product, data science, and business stakeholders. If there are any additional details about the company in the job description, please provide them as well. As a Full-Stack AI Architect, you will play a crucial role in designing, building, and scaling end-to-end AI-driven applications. Your responsibilities will span across software architecture, full-stack engineering, and applied AI/ML system design. You will be expected to translate business problems into scalable AI solutions, own the technical blueprint encompassing frontend, backend, data, and AI layers, and lead teams through implementation and productionization. - Key Responsibilities: - Design end-to-end AI system architectures covering data ingestion, model training/inference, APIs, UI, and deployment. - Select appropriate ML/LLM architectures based on use cases and define model lifecycle management. - Architect scalable, secure, and cost-efficient AI platforms. - Design and develop high-performance backend services using Python/Java/Node.js. - Build REST/GraphQL APIs for AI services and enterprise integrations. - Implement authentication, authorization, observability, and fault tolerance. - Optimize latency and throughput for real-time AI inference. - Guide development of AI-enabled user interfaces using React, Angular, or Vue. - Enable conversational, analytical, or decision-support experiences. - Ensure explainability and usability of AI outputs for end users. - Design data pipelines using SQL/NoSQL, data lakes, and warehouses. - Implement MLOps pipelines for model lifecycle management. - Ensure data quality, governance, and compliance. - Required Skills & Qualifications: - Strong experience in Python and one backend language (Java/Node.js). - Expertise in ML/DL frameworks such as TensorFlow, PyTorch, and scikit-learn. - Hands-on experience with LLMs, GenAI, RAG, embeddings, and vector databases. - Solid knowledge of frontend frameworks, with React preferred. - Strong API design and distributed systems knowledge. - Technical Leadership: - Act as a technical authority across AI and full-stack engineering. - Review designs, mentor engineers, and set coding/architecture standards. - Collaborate with product, data science, and business stakeholders. If there are any additional details about the company in the job description, please provide them as well.