GEN AI

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.