We are seeking a highly skilled and experienced Senior Data Scientist with a strong focus on Generative AI solutions. The ideal candidate will have extensive experience in designing, developing, and deploying Generative AI use cases both on-premises and on cloud platforms. This role requires proficiency in working with GPUs for scalable solutions, as well as expertise in Agentic AI, Generative AI, and related frameworks.
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Responsibilities
- Design, develop, and deploy Generative AI models and solutions on both on-premises and cloud platforms.
- Lead end-to-end Generative AI projects, from ideation, finetuning, evaluation, to deployment.
- Collaborate with cross-functional teams to integrate Generative AI solutions into existing systems and workflows.
- Continuously monitor and improve the performance of deployed models through rigorous testing and validation.
- Provide technical leadership and mentorship to junior data scientists and engineers.
- Stay up-to-date with the latest advancements in Generative AI and related technologies and apply them to solve complex business problems.
Requirements
- Bachelor's Degree in Artificial Intelligence, Business Analytics, Data Science, Computer Science, Mathematics, Engineering or related field.
- Master/PhD in Computer Science/ Machine learning or related field with more than 5 years of industry experience.
- Strong experience in Generative AI and data science, with a focus on developing and deploying models on both on-premises and cloud platforms.
- Proficiency in programming languages such as Python, R, or Scala.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, including services like AWS SageMaker, Azure ML, or Google AI Platform.
- Deep understanding of Generative AI algorithms, including GANs, VAEs, and transformers.
- Experience with GPU-based computing for scalable AI solutions.
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration skills.
Nice-to-Have Skills
- Experience with Agentic AI and related frameworks.
- Knowledge of MLOps and CI/CD pipelines for machine learning model deployment.
- Familiarity with containerization technologies such as Docker and Kubernetes.
- Experience with natural language processing (NLP) and computer vision.
- Knowledge of advanced statistical techniques and their applications.
- Experience with real-time data processing and streaming technologies.
