Senior Data Science Consultant

Role Overview: You are a highly skilled and experienced Senior Data Scientist with a minimum of 3-7 years of experience in Data Science and Machine Learning. 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: - 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 3-7 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. Role Overview: You are a highly skilled and experienced Senior Data Scientist with a minimum of 3-7 years of experience in Data Science and M