AI Engineer - Banking

Responsibilities:

• Collaborate with Global region stakeholders to identify Business needs and translate them into workable
production ready solution.
• Creating data transformation infrastructure, managing data ingestion, for AI/ML related processes
• Transforming models into production-ready APIs, microservices, and software applications. Monitoring model
performance, ensuring scalability, and updating systems to maintain accuracy.
• Building and maintaining the necessary IT infrastructure (cloud platforms like AWS/GCP, containerization) for AI
development.
• Working with data scientists, engineers, and product managers to define AI strategies and implement features.
• Ensure robust documentation of AI processes, standards, and controls in line with the Bank’s data governance
policies.
• Participate in Agile ceremonies and contribute to sprint planning, backlog grooming, and delivery cycles.
• Stay current with emerging AI trends, including Generative AI and large language models (LLMs), and be prepared
to integrate these advanced techniques into solutions where they can drive significant business value.
• Support the production of scalable and optimized AI/machine learning (ML) models
• Focus on building algorithms for the extraction, transformation and loading of large volumes of real time,
unstructured data to deploy AI/ML solutions
• Run experiments to test the performance of deployed models and identifies and resolves bugs that arise in the
process.
• Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the
firm.
• Work with the relevant software platforms in which the models are deployed.

Qualifications:

• Bachelor’s or Master’s degree in Artificial Intelligence / Data Science / Computer Science / Information
Technology / Programming & System Analysis / Computer Studies / data science or a related field, with a
minimum of 3 years of professional experience as a AI Engineer
• Proficient in Python, with a strong command of advanced syntax, popular libraries (e.g., Scikit-Learn), and the
ability to extend existing structures. Exhibits proficiency in Object-Oriented Programming (OOP) by implementing
S.O.L.I.D. principles and in data structures & algorithms by analyzing complexities and making efficient choices
(e.g., Numpy vs. Pandas).
• Proficient in applying Generative AI by using, understanding, and tuning large language models (LLMs) for diverse
scenarios. Skilled in architecting solutions that augment core models with external logic and tools (e.g.,
Retrieval-Augmented Generation or MCP) and in developing complex, end-to-end multi-agent systems using
services like ADK, DialogFlow, or equivalent cloud services.
• Demonstrates basic knowledge and practical experience in core software engineering practices, including
navigating operating systems, programming and querying languages (e.g., Java, SQL), version control (e.g., Git),
development methodologies (Agile, Waterfall), CI/CD concepts (e.g., Jenkins), testing principles, and
monitoring. Possesses a foundational understanding of design patterns, software architecture, and core cloud
technologies (e.g., GCP, AWS, or Azure).
• Demonstrates basic knowledge in the theoretical and practical application of machine learning.
• Capable of conducting code reviews for team members if needed
• Shows proficiency in the end-to-end data lifecycle, including advanced data engineering across traditional and
cloud databases with a focus on query optimization. Highly skilled in data preprocessing, from comprehensive
cleaning and encoding to advanced feature creation. A proficient communicator, capable of translating complex
technical findings into clear, influential data stories for diverse audiences, including senior leadership. Possesses
a foundational ability to create data visualizations (e.g., using Tableau) to support insights.
• Strong analytical and problem-solving skills and Excellent communication skills, both written and verbal.
• Ability to work independently and collaboratively in the context of global cross-functional teams. Familiarity with
Agile methodologies and user story documentation.