Generative AI Engineer

Role & responsibilities:

• Strong understanding of machine learning and deep learning principles and algorithms.
• Experience in developing and implementing generative AI models and algorithms. Proficiency in programming languages such as Python, TensorFlow, and PyTorch.
• Ability to work with large datasets and knowledge of data preprocessing techniques. Familiarity with natural language processing (NLP) and computer vision for generative AI applications.
• Experience in building and deploying generative AI systems in real-world applications. Strong problem-solving and critical thinking skills for complex AI problems.
• Experience building scalable data models and performing complex relational databases queries using SQL (BigQuery, Cloud SQL, Postgres), etc.
• Experience working with google cloud computing platforms and containerization technologies (e.g., Docker, Kubernetes).
• Experience on data extraction/manipulation experience using scripts specific to AI/ML.
• Hands-on experience with web APIs, CI/CD for ML, and Serverless Deployment.
• Experience with debugging and troubleshooting software or solutions design issues.

Preferred candidate profile

1.Working across teams to develop and architect Generative AI solutions using ML and GenAI.
2.Developing and promoting standards across projects.3.Evaluating and selecting appropriate AI tools and machine learning models for tasks, as well as building and training working versions of those models using Python and other open-source technologies.4.Working with leadership and stakeholders to identify AI opportunities and promote strategy.5. Building and prioritizing backlog for future machine-lea1.rning enabled features to support client business processes.6.Design and build generative models, selecting the most suitable architecture (e.g., GANs, VAEs) based on the desired output (text, images, code). This involves writing code using Python libraries like TensorFlow or PyTorch.7.Once model is built, train it on the prepared data, fine-tuning hyperparameters to achieve optimal performance then evaluate the model's outputs to assess its effectiveness and identify areas for improvement.8.Collaborate with other engineers to integrate generative AI solution into existing systems or develop new applications. This might involve deploying the model on cloud platforms for scalability.

9.The field of generative AI is rapidly evolving. Staying abreast of the latest research, advancements, and ethical considerations in AI development is an ongoing process.
10.Implement large-scale data ecosystems including data management, governance and the integration of structured and unstructured data to generate insights leveraging cloud-based platforms.11.Leverage automation, cognitive and science-based techniques to manage data, predict scenarios and prescribe actions.