Senior Data Scientist

Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities:Contribute to the design and implementation of state-of-the-art AI solutions.Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.Collaborate with stakeholders to identify business opportunities and define AI project goals.Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.Ensure compliance with data privacy, security, and ethical considerations in AI applications.Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements:Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus.Minimum 4 years of experience in Data Science and Machine Learning.In-depth knowledge of machine learning, deep learning, and generative AI techniques.Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch.Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.Familiarity with computer vision techniques for image recognition, object detection, or image generation.Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.Expertise in data engineering, including data curation, cleaning, and preprocessing.Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models.Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels.Understanding of data privacy, security, and ethical considerations in AI applications.Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills:Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems.Utilize optimization tools and techniques, including MIP (Mixed Integer Programming).Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models.Implement CI/CD pipelines for streamlined model deployment and scaling processes.Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation.Implement monitoring and logging tools to ensure AI model performance and reliability.Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment.Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.