As a Generative AI Solution Architect, you will be at the forefront of leveraging Generative Artificial Intelligence technologies to design and implement innovative solutions. This role requires a deep understanding of generative AI algorithms, creative problem-solving skills, and the ability to collaborate with interdisciplinary teams to bring forth novel applications in diverse industries.
Responsibilities
- Define and articulate clear requirements for generative AI solutions based on business needs.
- Architect end-to-end solutions that leverage generative AI technologies. Approach solutions that could be POC-led, platform-led, or integrated end-to-end solutions.
- Evaluate and select appropriate generative AI solutions and environments based on customer and project requirements. Finalization on estimations that are optimized from a performance, scale, efficiency, and fit the business case.
- Work closely with data scientists, machine learning engineers, and domain experts to integrate generative AI into diverse applications.
- Collaborate with creative professionals to ensure the alignment of generative outputs with artistic and design goals.
- Integrate ethical considerations into the solution design and deployment of generative AI solutions, addressing issues such as bias, fairness, and responsible use.
- Stay informed about ethical guidelines and best practices in the field of Generative AI.
- Ability to identify, attract, onboard, and train skills for execution of customer-led POCs and projects within agreed timelines.
Qualifications
- Proven experience in implementing and deploying generative AI models in practical applications.
- Strong programming skills in languages like Python, TensorFlow, PyTorch, or similar AI frameworks.
- Solid understanding of deep learning architectures and generative models such as GANs, VAEs, etc.
- Familiarity with natural language processing (NLP) and computer vision (CV) applications in generative AI.
- Experience in data pre-processing, feature engineering, and data augmentation for training generative models.
- Proficiency in data manipulation, statistical analysis, and model evaluation techniques.
- Passion for continuous learning and a keen interest in exploring the potential of generative AI in various industries.