Solution/Tech Architect - AI

• Solution Design: Design end-to-end AI solutions encompassing data ingestion, preprocessing, model development, deployment, and monitoring, ensuring scalability, reliability, and performance.
• Requirement Analysis: Collaborate with stakeholders to gather and analyze requirements, identifying opportunities for AI integration to address business challenges and achieve strategic goals.
• Technology Evaluation: Research and evaluate emerging AI technologies, frameworks, and tools to recommend optimal solutions that align with project requirements, budget constraints, and future scalability.
• Architecture Development: Define system architectures, data flow diagrams, and technical specifications for AI solutions, considering factors such as security, compliance, and interoperability with existing systems.
• Integration & Deployment: Oversee the integration of AI solutions with existing infrastructure, applications, and workflows, collaborating with DevOps and IT teams to ensure seamless deployment and maintenance.
• Performance Optimization: Identify bottlenecks and optimize AI algorithms, workflows, and infrastructure to enhance system performance, scalability, and cost-effectiveness.
• Quality Assurance: Establish testing strategies and quality assurance processes to validate the accuracy, reliability, and robustness of AI solutions, conducting thorough testing and debugging as needed.
• Documentation & Training: Create comprehensive documentation, user guides, and training materials for AI solutions, facilitating knowledge transfer and ensuring effective adoption by end-users and stakeholders.
• Continuous Improvement: Stay abreast of industry trends, best practices, and advancements in Cloud and AI technologies, advocating for innovation and driving continuous improvement in solution architecture and development processes.

• Proven experience (3 years) in designing and implementing AI solutions, with expertise in machine learning, deep learning, natural language processing and more than 5 years of experience as a Solution Architect.
• Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and AI services (e.g., VertexAI, OpenAI, SageMaker) for building and deploying AI solutions at scale.
• In-depth knowledge of software architecture principles, microservices architecture, containerization (e.g., Docker, Kubernetes), and distributed computing.
• A passion for Generative AI, and an understanding of strengths and weaknesses of Generative LLM's
• OWASP and Application security
• Strong proficiency in programming languages such as Python, R, Java, C# or Scala, with experience in frameworks like TensorFlow, PyTorch, scikit-learn, or Keras.
• Excellent problem-solving skills, analytical thinking, and attention to detail, with the ability to translate complex requirements into practical AI solutions.
• Strong communication and collaboration skills, with the ability to effectively interact with stakeholders across different levels of the organization.
• Relevant certifications (e.g., AWS Certified Solutions Architect, Google Cloud Certified - Professional Cloud Architect) are a plus.

• Experience in MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).
• Expertise in conversational system architecture
• Knowledge of big data technologies
• Knowledge on Statistical Methods
• Knowledge on Linux / Unix Flavors.

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