Senior Data Scientist - GenAI & Machine Learning

As an Experienced Data Scientist at our company, you will be responsible for developing and implementing Generative AI solutions to address critical business challenges and unlock new opportunities. You will leverage your expertise in data science, machine learning, and Generative AI techniques to drive impactful projects from ideation to deployment. **Responsibilities:** - **Data Handling & Preparation:** - Work with diverse and large-scale datasets. - Perform data cleaning, preprocessing, feature engineering. - Create synthetic datasets as needed for training and evaluating Generative AI models. - Ensure data quality and integrity throughout the model development lifecycle. - **Model Optimization & Deployment:** - Optimize Generative AI models for performance, scalability, efficiency. - Address responsible AI considerations such as bias detection and mitigation. - Collaborate with Machine Learning Engineers and DevOps teams for model deployment. - Establish monitoring frameworks for deployed models. - **Prompt Engineering & Fine-tuning:** - Develop effective prompts and fine-tuning strategies for Large Language Models. - Adapt models to specific domains and tasks to achieve desired outputs. - **Generative AI Model Development & Implementation:** - Design, develop, train, fine-tune, and deploy various Generative AI models. - Models may include Large Language Models, diffusion models, GANs, VAEs. - Applications include text generation, image and video synthesis, code generation, synthetic data generation. - **Research & Innovation:** - Stay current with the latest research in Generative AI. - Explore new architectures, methodologies, and tools. - Identify opportunities to apply cutting-edge techniques to solve business problems. - **Experimentation & Evaluation:** - Design and execute rigorous experiments to evaluate model performance. - Use relevant metrics such as BLEU, ROUGE, FID, perplexity. - Analyze results, identify areas for improvement, and iterate on model development. - **Collaboration & Communication:** - Work closely with cross-functional teams to understand requirements. - Communicate technical findings to non-technical audiences. - Translate complex AI concepts into understandable insights for stakeholders. - **Documentation & Knowledge Sharing:** - Maintain comprehensive documentation of development processes and models. - Share knowledge and best practices with data science and engineering teams. - **Ethical AI Practices:** - Adhere to ethical AI principles in model development and deployment. - Focus on fairness, transparency, and accountability in Generative AI projects.,