About Job/Role
An AI architect is responsible for designing, developing, and implementing artificial intelligence (AI) infrastructure within an organization, ensuring optimal integration of AI technology to transform business processes and drive innovation by leveraging machine learning, data science, and related technologies, while considering ethical considerations and best practices; essentially acting as a bridge between business needs and AI capabilities.

Roles and Responsibilities

Strategic planning: Define the overall AI strategy, identifying potential use cases and business opportunities for AI implementation across various departments. AI architects must also understand AI integration and AI application programming.
Architecture design: Develop and document the technical architecture for AI systems, including data pipelines, model selection, training infrastructure and deployment environments.
Technology selection: Evaluate and choose appropriate AI tools, frameworks, and platforms based on specific project requirements and organizational needs.
Data management: Oversee data collection, cleaning, labeling, and preparation for AI model training, ensuring data quality and governance.
Model development: Work with data scientists and machine learning engineers to design, train, and optimize AI models, including deep learning algorithms.
Integration and deployment: Integrate AI models into existing systems and applications, ensuring seamless functionality and scalability.
Performance monitoring: Monitor AI model performance in real-world scenarios, identify potential issues and implement necessary adjustments.
Ethical considerations: Adhere to ethical guidelines related to data privacy, bias detection and responsible AI development.

Required Qualification / Desired Skills

Bachelor's degree in Computer Science, Engineering or a related field.
5+ years of proven experience in big data, analytics and data science. Previous experience as an AI engineer will be a good experience.
Certification in Google Cloud AI platform or Azure AI engineer or similar AI tools will be an added skill.
Deep understanding of machine learning algorithms, statistical analysis, natural language processing, computer vision and related AI technologies.
Expertise in programming languages like Python, Java and experience with cloud computing platforms like AWS, Azure, or GCP.
Strong data analysis skills including data cleaning, feature engineering, and model evaluation.
Ability to design and implement complex distributed systems, considering scalability and performance optimization.
Understanding of business needs and ability to translate them into actionable AI solutions.
Effective communication with both technical and non-technical stakeholders to explain complex AI concepts.
Comfortable working for US shifts or any other shifts as per business needs.

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