Datasphereglobal
Technology :
Python, GCP, Docker, AWS, TypeScript, Azure
Role :
Evaluate, develop, and support a variety of machine learning model types, along with various NLP data pipelines to co-create new AI products. Apply state-of-the-art GenAI/ML/NLP and full-stack software engineering techniques to develop end-to-end intelligent solutions for unique business problems. Set up fine-tuning and evaluation pipelines on AWS, GCP, and other compute providers. Manage AI workload compute resources and monitor experiments, keeping track of results. Design, build, and maintain highly scalable, cloud-based services using TypeScript, Python, and React. Provide hands-on technical guidance and leadership throughout the lifecycle of GenAI/NLP-based projects. Create tooling to support the ML model lifecycle, including model evaluations, dataset curation, and training infrastructure. Build scalable infrastructure for LLM model orchestration with a focus on an intuitive user experience. Make architecture and technology decisions that balance business needs, innovation, security, and reliability. Enhance platform performance and scalability, focusing on creating seamless user experiences. Collaborate with cross-functional teams to deliver AI-powered products. Qualifications :
Degree in Data Science, Computer Science, Informatics, Life Sciences, Physics, Applied Mathematics, Statistics, or a related field. Proficiency in leveraging cloud-based machine learning resources such as AWS or Google Cloud for model training and productization. Strong understanding of Software Engineering and Agile Software Development Life Cycle principles. Expertise in Python, with good proficiency in SQL, Scala, or Java. Responsibilities:
Work with large datasets, build and evaluate models, and integrate them with other systems. Strong understanding of machine learning algorithms, model deployment, and monitoring. 2-5 years of experience as a Data Scientist, Machine Learning Engineer, or NLP Engineer. 2-5 years of experience working with structured, semi-structured, and unstructured datasets. Deep hands-on experience with LLMs and GenAI concepts (e.g., prompt engineering, RAG, GraphRAG, fine-tuning). Proficiency in cloud-based machine learning resources for model training and productization (AWS, GCP). Expertise in Python, with proficiency in SQL, Scala, or Java. Ability to work in a fast-paced environment, managing multiple projects and effectively communicating with diverse teams. Model Development : Design, develop, and implement machine learning models to solve business challenges. Model Evaluation and Optimization : Evaluate model performance, fine-tune parameters, and optimize models for accuracy and efficiency. AI/ML Deployment : Develop and deploy AI/ML models using AWS AI/ML services (e.g., SageMaker, Rekognition, Comprehend) and collaborate with data scientists and engineers. Containerization : Design, implement, and manage containerized applications using AWS Cloud Containerization services (e.g., ECS, EKS) and Docker, developing CI/CD pipelines for automated deployment and scaling. Presentation Skills :
Ability to develop visually simple and appealing PowerPoint presentations. Comfort with articulating and communicating key messages to a broad range of stakeholders.
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Evaluate, develop, and support a variety of machine learning model types, along with various NLP data pipelines to co-create new AI products. Apply state-of-the-art GenAI/ML/NLP and full-stack software engineering techniques to develop end-to-end intelligent solutions for unique business problems. Set up fine-tuning and evaluation pipelines on AWS, GCP, and other compute providers. Manage AI workload compute resources and monitor experiments, keeping track of results. Design, build, and maintain highly scalable, cloud-based services using TypeScript, Python, and React. Provide hands-on technical guidance and leadership throughout the lifecycle of GenAI/NLP-based projects. Create tooling to support the ML model lifecycle, including model evaluations, dataset curation, and training infrastructure. Build scalable infrastructure for LLM model orchestration with a focus on an intuitive user experience. Make architecture and technology decisions that balance business needs, innovation, security, and reliability. Enhance platform performance and scalability, focusing on creating seamless user experiences. Collaborate with cross-functional teams to deliver AI-powered products. Qualifications :
Degree in Data Science, Computer Science, Informatics, Life Sciences, Physics, Applied Mathematics, Statistics, or a related field. Proficiency in leveraging cloud-based machine learning resources such as AWS or Google Cloud for model training and productization. Strong understanding of Software Engineering and Agile Software Development Life Cycle principles. Expertise in Python, with good proficiency in SQL, Scala, or Java. Responsibilities:
Work with large datasets, build and evaluate models, and integrate them with other systems. Strong understanding of machine learning algorithms, model deployment, and monitoring. 2-5 years of experience as a Data Scientist, Machine Learning Engineer, or NLP Engineer. 2-5 years of experience working with structured, semi-structured, and unstructured datasets. Deep hands-on experience with LLMs and GenAI concepts (e.g., prompt engineering, RAG, GraphRAG, fine-tuning). Proficiency in cloud-based machine learning resources for model training and productization (AWS, GCP). Expertise in Python, with proficiency in SQL, Scala, or Java. Ability to work in a fast-paced environment, managing multiple projects and effectively communicating with diverse teams. Model Development : Design, develop, and implement machine learning models to solve business challenges. Model Evaluation and Optimization : Evaluate model performance, fine-tune parameters, and optimize models for accuracy and efficiency. AI/ML Deployment : Develop and deploy AI/ML models using AWS AI/ML services (e.g., SageMaker, Rekognition, Comprehend) and collaborate with data scientists and engineers. Containerization : Design, implement, and manage containerized applications using AWS Cloud Containerization services (e.g., ECS, EKS) and Docker, developing CI/CD pipelines for automated deployment and scaling. Presentation Skills :
Ability to develop visually simple and appealing PowerPoint presentations. Comfort with articulating and communicating key messages to a broad range of stakeholders.
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