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Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 7 - 12 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 7-12 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.
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