Collaborate with cross-functional teams including product managers, engineers, data scientists, and subject matter experts to define requirements and prioritize AI initiatives.
Define the technical architecture and infrastructure required for AI-driven applications and platforms.
Design and develop scalable AI models, algorithms, and systems that support personalized learning, adaptive assessments, and intelligent tutoring systems.
Provide technical leadership and guidance to development teams in the implementation of AI algorithms and systems.
Mitigate risks by identifying potential challenges and proposing solutions or alternative approaches.
Facilitate effective communication and collaboration among cross-functional teams, including business stakeholders, developers, testers, and infrastructure teams.
Conduct system assessments and performance reviews to identify areas for improvement and optimization.
Stay up to date with the latest advancements in AI technologies, frameworks, and tools.
Contribute to the development and maintenance of architectural frameworks, guidelines, and standards within the organization.
Drive innovation by identifying opportunities to leverage AI and machine learning techniques to improve learning outcomes.
Ensure compliance with security, privacy, and regulatory requirements in the design and implementation of solutions.
Collaborate with project managers to define project scope, timelines, and deliverables, and provide technical input for project planning and estimation
Requirements & Skills:
System architecture design and modelling
Proficiency in one or more programming languages commonly used in AI development, such as Python, R, and Java
Proficiency in AI and ML concepts, algorithms, and frameworks, such as TensorFlow, PyTorch, sci-kit-learn, and Keras
Application of AI to support adaptive learning, personalized feedback, intelligent tutoring, and assessment in alignment with pedagogical practices
Strong knowledge of AI techniques, including machine learning, natural language processing, computer vision, and recommendation systems
Strong understanding of generative & conversational AI and associated concepts, such as input safeguarding, topic rails, scenario validation, safety verification
Solid understanding of data engineering principles, data pipelines, and data preprocessing techniques
Database management systems (MySQL, SQL Server, MongoDB), Data modelling, and database design
Cloud computing platforms and services (AWS, Azure, GCP)
Integration technologies: ESB, message queues, API gateways
Security principles and best practices, including authentication, authorization, encryption
DevOps practices and tools (CI/CD, version control, automated testing)
Performance optimization techniques, load balancing, scalability, and distributed system design principles
Microservices architecture and design principles
Understanding of software development lifecycle (SDLC) and Agile methodologies
Experience with security frameworks and technologies
Understanding of network protocols (TCP/IP, HTTP, REST)
Knowledge of data integration techniques and protocols (SOAP, REST, XML, JSON)
Familiarity with containerization and orchestration technologies (Docker, Kubernetes)
Understanding of virtualization, storage, and networking
