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FirstRand - Bellville, TX

posted 17 days ago

- Mid Level
Bellville, TX
Credit Intermediation and Related Activities

About the position

We are seeking a skilled AI Practitioner/Data Scientist with a deep specialization in Generative AI and a robust background in data. The ideal candidate will have hands-on experience in designing, developing, and deploying AI models with a focus on generative techniques. This role demands a strong understanding of data engineering principles, making the candidate well-equipped to handle the data pipeline challenges inherent in AI and data projects. GenAI Model Development: * Design, implement, and optimize operational and analytical solutions utilizing Generative AI models. * Develop and fine-tune RAG (Retrieval Augmented Generation) frameworks utilizing LLM frameworks such as LangChain, Llama-Index or other generative frameworks. * Experiment with new architectures and techniques to push the boundaries of generative AI. Data Engineering: * Collaborate with data engineering/analyst teams to ensure the availability and quality of data needed for model training and validation. * Design and maintain scalable data pipelines to handle large volumes of structured and unstructured data. * Perform ETL tasks with to create optimal data-warehousing structures (Dimensional modelling) for optimal storage, ease of use and maintainability. * Integrate data from various sources and ensure its proper storage, transformation, and accessibility. Deployment and Scaling: * Deploy AI and generative AI models in production environments, ensuring they meet performance, scalability, and reliability requirements. * Work with DevOps teams to automate the deployment and monitoring of AI models. * Optimize model inference for performance and cost efficiency in cloud and on-premises environments. Collaboration and Communication: * Collaborate with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders, to align AI solutions with business objectives. * Communicate complex AI concepts and findings to non-technical stakeholders. Continuous Learning and Innovation: * Stay up to date with the latest advancements in Generative AI and data engineering. * Participate in conferences, workshops, and other professional development opportunities. * Contribute to research publications and patent filings in the field of Generative AI.

Responsibilities

  • Design, implement, and optimize operational and analytical solutions utilizing Generative AI models.
  • Develop and fine-tune RAG (Retrieval Augmented Generation) frameworks utilizing LLM frameworks such as LangChain, Llama-Index or other generative frameworks.
  • Experiment with new architectures and techniques to push the boundaries of generative AI.
  • Collaborate with data engineering/analyst teams to ensure the availability and quality of data needed for model training and validation.
  • Design and maintain scalable data pipelines to handle large volumes of structured and unstructured data.
  • Perform ETL tasks to create optimal data-warehousing structures (Dimensional modelling) for optimal storage, ease of use and maintainability.
  • Integrate data from various sources and ensure its proper storage, transformation, and accessibility.
  • Deploy AI and generative AI models in production environments, ensuring they meet performance, scalability, and reliability requirements.
  • Work with DevOps teams to automate the deployment and monitoring of AI models.
  • Optimize model inference for performance and cost efficiency in cloud and on-premises environments.
  • Collaborate with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders, to align AI solutions with business objectives.
  • Communicate complex AI concepts and findings to non-technical stakeholders.
  • Stay up to date with the latest advancements in Generative AI and data engineering.
  • Participate in conferences, workshops, and other professional development opportunities.
  • Contribute to research publications and patent filings in the field of Generative AI.

Requirements

  • 3+ years of experience in AI, with a focus on Generative AI (1-2 years).
  • Proven experience in data engineering, including data pipeline development, ETL processes, and database management.
  • Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and Generative AI frameworks.
  • Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
  • Proficiency in programming languages such as Python, SQL, and familiarity with data manipulation libraries (e.g., Pandas, NumPy).
  • Strong knowledge of machine learning algorithms, neural networks, and generative models.
  • Experience with data storage solutions (e.g., Hadoop, Spark, Vector databases).
  • Knowledge of MLOps practices, including model versioning, monitoring, and retraining.

Nice-to-haves

  • Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or a related field.
  • Master's or Ph.D. is advantageous.
  • AWS Certified AI Practitioner
  • AWS Certified Machine Learning
  • GCP Professional Machine Learning Engineer
  • GCP Associate Cloud Engineer
  • Microsoft Azure AI engineer
  • Microsoft Azure Data Science.
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