Overview The AI Solution Architect will be responsible for designing and guiding the technical architecture for the rapid delivery of Machine Learning (ML) and Large Language Model (LLM) powered applications. This role requires a strong understanding of end-to-end system design, integration principles, and the ability to translate business needs into clear and scalable technical blueprints. The architect will ensure seamless integration of various components, optimize for scalability, and effectively communicate architectural designs and trade-offs to both technical and non-technical stakeholders. Key Responsibilities: Design and document end-to-end technical architectures for rapid prototyping and Proof-of-Concepts (POCs) of ML and LLM powered applications. Translate complex business requirements into actionable and well-defined technical designs, considering factors such as performance, scalability, security, and maintainability. Architect the integration of various systems, including APIs (REST, gRPC, etc.), databases (SQL, NoSQL), and front-end applications, within tight project timelines and constraints. Define data pipelines and data storage solutions optimized for ML/LLM workloads. Select appropriate technologies, frameworks, and tools for building and deploying AI solutions. Ensure the scalability and reliability of the designed architectures. Collaborate closely with development teams to provide architectural guidance and support throughout the development lifecycle. Communicate architectural designs, trade-offs, and technical complexities clearly and concisely to both technical and non-technical stakeholders, including product managers, business analysts, and executives. Identify and mitigate potential technical risks and challenges. Stay up-to-date with the latest advancements in AI/ML/LLM technologies and architectural patterns. Contribute to the development of architectural best practices and standards within the organization. Requirements Skills & Experience: Proven experience in designing end-to-end technical architectures for rapid prototyping or POCs, with a strong emphasis on ML/LLM components. Demonstrated ability to break down complex business requirements into clear, actionable technical designs and specifications. Hands-on experience integrating multiple systems, including APIs (REST, gRPC, etc.), databases (SQL, NoSQL), and front-end applications, under tight timelines and constraints. Excellent communication and presentation skills, with the ability to effectively articulate complex technical concepts and architectural trade-offs to both technical and non-technical team members. Strong understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and their AI/ML services. Experience with containerization technologies (e.g., Docker, Kubernetes). Familiarity with data engineering principles and tools. Understanding of software development methodologies (e.g., Agile, Scrum). Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field. Preferred Qualifications: Experience with specific ML/LLM frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers). Experience with MLOps practices and tools for deploying and managing ML models. Knowledge of security best practices for AI applications. Experience in a fast-paced startup or innovation-driven environment.