Society of Exploration Geophysicists
GenAI Data Scientist
Society of Exploration Geophysicists, Irving, Texas, United States, 75084
Title: GenAI Data ScientistFulltimeLocation: Irving, TX / Piscataway, New JerseyRole and Responsibilities
Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.Utilize, apply & enhance GenAI models using state-of-the-art techniques like transformers, GANs, VAEs, LLMs (including experience with various LLM architectures and capabilities), and vector representations for efficient data processing.Implement and optimize GenAI models for performance, scalability, and efficiency, considering factors like chunking strategies for large datasets and efficient memory management.Integrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and RAG workflows, ensuring seamless data flow and information exchange.Develop user-facing interfaces (UIs) using modern front-end frameworks (e.g., React, Angular) to deliver an intuitive and interactive experience for RAG applications.Develop robust APIs (RESTful or GraphQL) using back-end frameworks (e.g., Django, Node.js) to facilitate communication between the front-end UI, GenAI models, and data sources.Utilize LangChain and similar tools (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.Apply software engineering principles to develop secure, scalable, maintainable, and production-ready GenAI applications.Build and deploy GenAI applications on cloud platforms (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.Integrate GenAI applications with other applications, tools, and analytical solutions (including dashboards and reporting tools) to create a cohesive user experience and workflow within the RAG ecosystem.Continuously evaluate and improve GenAI models, applications, and user interfaces based on data, feedback, user needs, and RAG application performance metrics.Stay up-to-date with the latest advancements in GenAI research, development, front-end and back-end development practices, integration tools, LLM architectures, and RAG functionalities.Document code, models, processes, UI/UX design choices, and RAG application design for future reference and knowledge sharing.Technical Skills Requirements
Strong understanding of machine learning and deep learning concepts.Proficiency in Python (libraries like TensorFlow, PyTorch) with experience in vector data manipulation libraries.Experience with generative AI models (transformers, GANs, VAEs) and various LLM architectures.Experience with front-end development frameworks (e.g., React, Angular) and UI/UX design principles.Experience with back-end development frameworks (e.g., Django, Flask) and API development (RESTful or GraphQL).Experience with NLP techniques (text cleaning, pre-processing, text analysis).Experience with software engineering principles and best practices (object-oriented programming, design patterns, testing).Familiarity with cloud platforms (AWS, Azure, or GCP).Knowledge of containerization technologies (Docker, Kubernetes).Experience with data integration tools and techniques (a plus).Knowledge of chunking strategies for handling large datasets.Experience working with RAG applications and their functionalities.Expertise in LangChain and similar tools (e.g., PromptChain) for prompt engineering and data processing in RAG applications.Experience with DevOps principles and tools for continuous integration and delivery (CI/CD).Experience with building and integrating with analytical dashboards and reporting tools.Nice-to-Have Skills
Experience working with RAG applications.Experience with cloud-based data warehousing solutions (e.g., BigQuery, Redshift, Snowflake).Experience with cloud-based workflow orchestration tools (e.g., Airflow, Prefect).Familiarity with Kubernetes (K8S) is a welcome addition.Google Cloud certification.Unix or Shell scripting.Qualifications
B.Tech., M.Tech. or MCA degree from a reputed university.
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Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.Utilize, apply & enhance GenAI models using state-of-the-art techniques like transformers, GANs, VAEs, LLMs (including experience with various LLM architectures and capabilities), and vector representations for efficient data processing.Implement and optimize GenAI models for performance, scalability, and efficiency, considering factors like chunking strategies for large datasets and efficient memory management.Integrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and RAG workflows, ensuring seamless data flow and information exchange.Develop user-facing interfaces (UIs) using modern front-end frameworks (e.g., React, Angular) to deliver an intuitive and interactive experience for RAG applications.Develop robust APIs (RESTful or GraphQL) using back-end frameworks (e.g., Django, Node.js) to facilitate communication between the front-end UI, GenAI models, and data sources.Utilize LangChain and similar tools (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.Apply software engineering principles to develop secure, scalable, maintainable, and production-ready GenAI applications.Build and deploy GenAI applications on cloud platforms (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.Integrate GenAI applications with other applications, tools, and analytical solutions (including dashboards and reporting tools) to create a cohesive user experience and workflow within the RAG ecosystem.Continuously evaluate and improve GenAI models, applications, and user interfaces based on data, feedback, user needs, and RAG application performance metrics.Stay up-to-date with the latest advancements in GenAI research, development, front-end and back-end development practices, integration tools, LLM architectures, and RAG functionalities.Document code, models, processes, UI/UX design choices, and RAG application design for future reference and knowledge sharing.Technical Skills Requirements
Strong understanding of machine learning and deep learning concepts.Proficiency in Python (libraries like TensorFlow, PyTorch) with experience in vector data manipulation libraries.Experience with generative AI models (transformers, GANs, VAEs) and various LLM architectures.Experience with front-end development frameworks (e.g., React, Angular) and UI/UX design principles.Experience with back-end development frameworks (e.g., Django, Flask) and API development (RESTful or GraphQL).Experience with NLP techniques (text cleaning, pre-processing, text analysis).Experience with software engineering principles and best practices (object-oriented programming, design patterns, testing).Familiarity with cloud platforms (AWS, Azure, or GCP).Knowledge of containerization technologies (Docker, Kubernetes).Experience with data integration tools and techniques (a plus).Knowledge of chunking strategies for handling large datasets.Experience working with RAG applications and their functionalities.Expertise in LangChain and similar tools (e.g., PromptChain) for prompt engineering and data processing in RAG applications.Experience with DevOps principles and tools for continuous integration and delivery (CI/CD).Experience with building and integrating with analytical dashboards and reporting tools.Nice-to-Have Skills
Experience working with RAG applications.Experience with cloud-based data warehousing solutions (e.g., BigQuery, Redshift, Snowflake).Experience with cloud-based workflow orchestration tools (e.g., Airflow, Prefect).Familiarity with Kubernetes (K8S) is a welcome addition.Google Cloud certification.Unix or Shell scripting.Qualifications
B.Tech., M.Tech. or MCA degree from a reputed university.
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