Artificial Intelligence Architect - Data Modeling

Role: AI Architect Experience: 7 to 12 Years Location: Chennai, India Purpose : As an experienced and visionary AI Architect within our AI Tech organization, you will be instrumental in shaping the future of our AI capabilities. You will take ownership of designing and implementing the overarching architecture for our diverse range of AI systems and solutions. This role demands a deep technical understanding of artificial intelligence and machine learning principles, coupled with significant practical experience in architecting and deploying complex AI solutions at scale. You will be a key technical leader, collaborating closely with cross-functional teams to ensure our AI initiatives are built on a robust, scalable, and future-proof foundation. Role and Responsibilities : - Architectural Design and Implementation : Lead the design and implementation of end-to-end architecture for our AI systems and solutions, encompassing data ingestion, processing, model development, deployment, and monitoring. - Standards and Governance : Develop, document, and maintain comprehensive architectural guidelines, patterns, and standards to ensure consistency, quality, and best practices across all AI projects. - Cross-Functional Collaboration : Collaborate effectively with AI engineers, data scientists, machine learning engineers, and other stakeholders to translate business requirements into robust and scalable AI architectures. Ensure seamless integration of AI components within the broader technology landscape. - Technology Evaluation and Recommendation : Continuously evaluate emerging AI technologies, tools, and platforms, providing informed recommendations for adoption to enhance our AI capabilities and drive innovation. - Architectural Roadmap and Evolution : Define and maintain a clear and forward-looking roadmap for the evolution of our AI architecture, anticipating future needs and technological advancements. - Staying Abreast of Innovation : Proactively stay updated with the latest research, trends, and advancements in artificial intelligence, machine learning, and related fields, and disseminate this knowledge within the team. - Stakeholder Engagement : Develop and maintain strong relationships with internal clients and stakeholders to understand their business needs, technical requirements, and constraints, ensuring that the AI architecture aligns with their objectives. - Performance and Scalability : Design AI architectures with a strong focus on performance, scalability, reliability, and security, ensuring they can handle growing data volumes and computational demands. - Cost Optimization : Consider cost-effectiveness in architectural designs, leveraging cloud resources and efficient deployment strategies to optimize infrastructure spending. - Mentorship and Guidance : Provide technical guidance and mentorship to AI engineers and other team members on architectural best practices and design patterns. - Documentation and Communication : Create and maintain comprehensive architectural diagrams, documentation, and presentations to effectively communicate complex technical concepts to both technical and non-technical and Education Requirements : Education: Master's or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field from a reputable institution. Experience : - Minimum of 7 to 12 years of hands-on experience in the field of Artificial Intelligence and Machine Learning. - Minimum of 3 years of demonstrable experience in designing and implementing complex and scalable AI architectures for real-world applications. Technical Skills : - Deep understanding of fundamental AI and machine learning concepts, algorithms, and techniques (e.g., deep learning, natural language processing, computer vision, reinforcement learning). - Strong proficiency in one or more programming languages commonly used in AI/ML development (e.g., Python, Java, Scala). - Extensive experience with cloud-based AI platforms and technologies (e.g., AWS AI/ML services, Azure AI, Google Cloud AI Platform). - Experience with containerization technologies (e.g., Docker, Kubernetes) and orchestration platforms. - Knowledge of data engineering principles and tools for data ingestion, processing, and storage (e.g., Spark, Kafka, data lakes). - Familiarity with MLOps practices and tools for model deployment, monitoring, and lifecycle management. - Understanding of API design and development for integrating AI solutions with other systems. - Knowledge of data security and privacy considerations in AI systems. (ref:hirist.tech)

Location: chennai, IN

Posted Date: 4/28/2025