Responsibilities:
1. Solution Architecture and Technical Design:
• Design and architect end-to-end AI solutions, including data pipelines, model development, deployment strategies, and integration with existing systems.
• Define the technical components, services, and libraries required for AI projects, ensuring scalability, security, and performance.
• Lead the selection of appropriate AI frameworks, tools, and platforms (e.g., TensorFlow, PyTorch, Databricks, Azure AI) to meet project requirements.
2. Hands-On Development:
• Actively participate in the development of AI models, writing code, building algorithms, and deploying models into production environments.
• Collaborate with data scientists and software engineers to implement AI solutions that are robust, scalable, and efficient.
• Ensure that the technical design is aligned with best practices for AI development, including the use of CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployment (Azure).
3. Technical Leadership:
• Provide technical guidance and mentorship to development teams, ensuring that they follow best practices in AI and software development.
• Review code, design, and architecture to ensure that the solutions meet KDN’s high standards for quality and security.
• Lead technical discussions in design and implementation phases, making critical decisions that impact the architecture and design of AI solutions.
4. Component and Service Design:
• Architect and design reusable components, microservices, and APIs that can be leveraged across multiple AI projects within KDN.
• Develop and maintain libraries of reusable code, tools, and templates that accelerate AI development and ensure consistency across projects.
• Ensure that all components are designed for integration with existing systems, supporting seamless data flow and interoperability.
5. Research and Innovation:
• Stay up-to-date with the latest advancements in AI, machine learning, deep learning, and cloud computing, bringing new ideas and technologies to the team.
• Experiment with emerging AI technologies, such as Generative AI, Reinforcement Learning, and Neural Architecture Search, to identify their potential applications within KDN.
• Lead the technical exploration of new AI use cases, developing prototypes and proof-of-concept solutions to validate their feasibility.
6. Collaboration and Communication:
• Work closely with stakeholders across KPMG member firms to understand business needs and translate them into technical solutions.
• Communicate complex technical concepts to non-technical stakeholders, ensuring that they understand the capabilities and limitations of AI technologies.
• Collaborate with external partners, including technology providers and academic institutions, to drive innovation and knowledge sharing.
7. Security and Compliance:
• Architect AI solutions with a strong focus on security, ensuring that data privacy and protection are built into the design.
• Implement compliance with industry standards and regulations (e.g., GDPR, ISO 27001), ensuring that AI solutions adhere to KDN’s legal and ethical guidelines. Qualifications
Educational qualifications
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• Advanced certifications in AI/ML, Cloud Computing, or Enterprise Architecture are highly desirable.
Work experience
• 15+ years of experience in software development, with at least 7 years focused on AI, machine learning, or related technologies.
• Proven experience in architecting and implementing AI solutions, including hands-on development with frameworks like TensorFlow, PyTorch, Keras.
• Extensive experience with cloud platforms, particularly Microsoft Azure, and expertise in deploying AI solutions in cloud environments.
• Strong background in DevOps practices, including CI/CD pipelines, Docker, and Kubernetes.
Skills
• Deep understanding of AI/ML algorithms, model development, and deployment strategies.
• Proficiency in programming languages such as Python, Java, or C++, with a focus on AI/ML libraries and frameworks.
• Strong problem-solving skills, with the ability to design and implement complex technical solutions.
• Excellent communication skills, with the ability to lead technical discussions and collaborate with cross-functional teams.
• Knowledge of enterprise architecture frameworks (TOGAF) and ITIL practices.