GEN AI

Job description

• The Data & Analytics team is responsible for integrating new data sources, creating data models,
• developing data dictionaries, and building machine learning models for Wholesale Bank. The
• primary objective is to design and deliver data products that assist squads at Wholesale Bank in
• achieving business outcomes and generating valuable business insights. Within this job family,
• we distinguish between Data Analysts and Data Scientists. Both roles work with data, write
• queries, collaborate with engineering teams to source relevant data, perform data munging
• (transforming data into a format suitable for analysis and interpretation), and extract meaningful
• insights from the data. Data Analysts typically work with relatively simple, structured SQL
• databases or other BI tools and packages. On the other hand, Data Scientists are expected to
• develop statistical models and be hands-on with machine learning and advanced programming,
• including Generative A Requirements
• We are seeking a highly skilled Data Science and Generative AI Specialist with 2+ years of
• experience in machine learning, deep learning, or AI research, with a focus on generative models.
• The ideal candidate will have strong expertise in data science, machine learning, and generative
• AI, with specific experience in document detail extraction, feature engineering, data processing
• using Python, and familiarity with tools such as Streamlit for data app creation. The candidate
• must also possess advanced skills in prompt engineering, chain of thought techniques, and AI
• agents to drive our cutting edge projects forward Key Responsibilities:
• Develop and implement machine learning models for document detail extraction and
• data processing.
• Perform feature engineering to enhance predictive models and data algorithms.
• Implement advanced data augmentation, feature extraction, and data transformation
• techniques to optimize the training process.
• Deploy generative AI models into production environments, ensuring they are scalable,
• efficient, and reliable for real time applications.
• Use cloud platforms (AWS, GCP, Azure) and containerization tools e.g., Docker,
• Kubernetes) for model deployment and scaling.
• Utilize Python and libraries such as pandas, NumPy, scikit learn, TensorFlow, and PyTorch
• for data analysis, processing, and model development.
• Create interactive data applications using Streamlit for various stakeholders. Qualifications:
• 2+ years of experience in machine learning, deep learning, or AI research, with a focus on
• generative models.
• Experience with generative models such as GANs (Generative Adversarial Networks),
• VAEs Variational Autoencoders), and transformer based models
• Understanding of model fine tuning, transfer learning, and prompt engineering in the
• context of large language models (LLMs).
• Knowledge of reinforcement learning (RL) and other advanced machine learning
• techniques applied to generative tasks. Strong programming skills in Python and familiarity with relevant libraries and
• frameworks.