KPMG Delivery Network (KDN) is seeking a highly skilled Generative AI Engineer to join the team. The Generative AI Engineer will play a pivotal role in designing, coding, and deploying advanced AI solutions using state-of-the-art technologies such as Databricks, AI Fabric, Azure, and Snowflake. This role requires a deep understanding of AI/ML frameworks and cloud-based environments, focusing on building scalable, high-performance AI solutions that drive value for KPMG’s global network.
Key responsibilities include:
AI Solution Development:- Design, develop, and deploy Generative AI models and solutions that address complex business challenges across advisory, tax, and audit services.
- Leverage platforms such as Databricks for data engineering and AI model development, AI Fabric for orchestration and deployment, and Snowflake for scalable data management.
- Utilize Azure cloud services to implement and scale AI solutions, ensuring high availability, performance, and security.
- Collaborate with data scientists, AI architects, and software engineers to define technical requirements and develop end-to-end AI solutions.
- Lead the development of AI models from experimentation and prototyping through to production, ensuring alignment with business objectives.
- Work closely with cross-functional teams to integrate AI solutions into existing workflows and systems, optimizing for efficiency and usability.
- Write high-quality, maintainable code using Python, Scala, or similar programming languages, focusing on AI/ML libraries and frameworks.
- Develop and optimize data pipelines using Databricks, ensuring seamless data flow from ingestion to AI model training and inference.
- Implement AI solutions using AI Fabric, focusing on model orchestration, deployment, and monitoring within a cloud environment.
- Design and manage data architectures using Snowflake, ensuring data is organized, accessible, and secure for AI model training and deployment.
- Integrate data from various sources, transforming and preparing it for AI model development, ensuring data quality and integrity.
- Work with large datasets, applying best practices for data engineering, ETL processes, and real-time data processing.
- Deploy AI models and services in Azure, utilizing cloud-native tools and best practices to ensure scalability, reliability, and security.
- Implement CI/CD pipelines to automate the deployment and management of AI models, ensuring rapid iteration and continuous delivery.
- Optimize infrastructure for AI workloads, balancing performance, cost, and resource utilization.
- Continuously monitor and optimize AI models and data pipelines to improve performance, accuracy, and scalability.
- Implement strategies for model fine-tuning, hyperparameter optimization, and feature engineering to enhance AI solution effectiveness.
- Troubleshoot and resolve technical issues related to AI model deployment, data processing, and cloud infrastructure.
- Stay updated with the latest advancements in Gen AI, cloud computing, and big data technologies, applying new techniques to improve solutions.
- Experiment with emerging technologies and frameworks to drive innovation.
- Contribute to the development of AI best practices, coding standards, and technical documentation to ensure consistency and quality across projects.
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 data engineering are advantageous.
Work experience
- 10+ years of experience in AI, Machine Learning, or related fields, with hands-on experience in developing and deploying AI solutions.
- Proven experience with AI frameworks such as TensorFlow, PyTorch, and experience in working with Databricks, AI Fabric, and Snowflake.
- Extensive experience with Azure cloud services, including AI and data services, and a strong background in cloud-native development.
- Expertise in coding with Python, Scala, or similar languages, with a focus on AI/ML libraries and big data processing.
Skills
- Proficiency in designing and coding AI models, data pipelines, and cloud-based solutions.
- Strong understanding of AI/ML algorithms, data engineering, and model deployment strategies.
- Experience with cloud infrastructure management, particularly in Azure, and the ability to optimize AI workloads for performance and cost.
- Excellent problem-solving skills and the ability to work collaboratively in a cross-functional team environment.
- Strong communication skills, with the ability to articulate complex technical concepts to technical and non-technical stakeholders.