Data Science

Job Summary We are seeking an experienced Generative AI and Data Science professional to contribute to the design, development, and deployment of AI-driven solutions and products. The ideal candidate will have strong expertise in Python, Data Science, Machine Learning, and Generative AI technologies, with the ability to translate business requirements into scalable and effective AI solutions. Key Responsibilities Design, develop, and deploy Generative AI solutions for business and client use cases Build and optimize AI/ML models, including fine-tuning and evaluation activities Develop and maintain data pipelines and AI workflows using Python Apply Data Science and Machine Learning techniques for data analysis, feature engineering, and model optimization Work with Large Language Models (LLMs) and Generative AI frameworks to develop scalable AI solutions Collaborate with cross-functional teams to integrate AI capabilities into products and platforms Participate in solution architecture discussions and provide technical guidance Conduct prompt engineering, model evaluation, and performance optimization activities Stay current with advancements in Generative AI, LLMs, and AI technologies Support end-to-end AI solution development and deployment initiatives Required Qualifications Strong experience in Python programming Solid background in Data Science and Machine Learning Hands-on experience with Generative AI technologies and Large Language Models (LLMs) Experience with AI frameworks and tools such as OpenAI, Hugging Face, LangChain, or similar technologies Experience developing end-to-end AI solutions or products Understanding of model training, fine-tuning, prompt engineering, and evaluation techniques Strong analytical, problem-solving, and data interpretation skills Ability to work independently and contribute to technical solution design Excellent verbal and written communication skills Ability to collaborate effectively with technical and business stakeholders Preferred Qualifications Experience with cloud platforms such as AWS, Azure, or GCP for AI workloads Exposure to MLOps practices and AI model deployment Experience working in client-facing or product-based environments Familiarity with scalable AI architecture and deployment strategies