The primary responsibility of the Solutions Architect is to consult with the client and work with internal teams to create strategies, transformational designs and the architectural vision for complex solutions that address the clients' needs. They provide multi-technology consulting services on all aspects of application software, infrastructure and related technology components.

Responsibilities:

• Contributes to the development of solution architectures in specific business, infrastructure or functional areas.
• Identifies and evaluates alternative architectures and the trade-offs in cost, performance and scalability.
• Produces specifications of cloud-based or on-premises components, tiers and interfaces, for translation into detailed designs using selected services and products.
• Supports a change programme or project through the preparation of technical plans and application of design principles that comply with enterprise and solution architecture standards.
• Takes responsibility for understanding client requirements, collecting data, delivering analysis and problem resolution.
• Monitors the external environment to gather intelligence on emerging technologies.
• Creates reports and technology roadmaps and shares knowledge and insights with others.
• Designs components using appropriate modelling techniques following agreed architectures, design standards, patterns and methodology.
• Creates multiple design views to address the concerns of the different stakeholders of the architecture and to handle both functional and non-functional requirements, including proof of concept (POC).
• Reviews, verifies and improves own designs against specifications.
• Contribute to the development of new go to market services and contributes knowledge on relevant verticals.
• Operate across both the technical levels within the enterprise architecture and the value chain in order to develop holistic and integrated solutions.

Requirements:

• Bachelor's degree in Computer Science, Artificial Intelligence, or a related field.
• Certifications in AI and machine learning, such as Google Cloud Professional Machine Learning Engineer or AWS Certified Machine Learning.
• Expertise in machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch).
• Proficiency in programming languages such as Python, R, and Java.
• Strong understanding of data preprocessing and feature Engineering.
• Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) for deploying AI solutions.
• Ability to design and implement neural networks and Deep Learning models.
• Strong analytical and critical thinking skills.