Design, develop, and maintain our data infrastructure, including data pipelines, databases, data warehouses, and data lakes, with a specific focus on financial services applications.
Collaborate closely with data scientists, financial analysts, and other stakeholders to gather and analyze data requirements for AI projects, ensuring the availability, quality, and relevance of financial data.
Implement scalable and efficient data processing and integration solutions to support AI model development and deployment, specifically tailored for financial services use cases.
Build and optimize AI data models, ensuring data integrity, accuracy, and compliance with regulatory requirements and financial industry standards.
Work closely with cross-functional teams to identify and integrate external financial data sources, such as market data feeds, transaction data, and economic indicators, for training and enhancing AI models.
Implement data governance practices and adhere to data security and privacy regulations, particularly within the financial services context.
Troubleshoot and resolve AI data-related issues, such as performance bottlenecks, data quality problems, and model performance discrepancies, with a strong focus on financial domain challenges.
Monitor and maintain AI data systems to ensure their availability, reliability, and performance, and proactively implement necessary optimizations and improvements.
Stay up-to-date with the latest trends and advancements in AI, machine learning, and financial technologies, and apply them to continuously improve our AI data infrastructure and processes.
Skills & Qualifications
Bachelor’s degree in computer science, Information Systems, or a related field. A master’s degree specializing in AI, machine learning, or Finance is highly preferred.
Proven experience as an AI Data Engineer or in a similar role, with a focus on financial services and AI applications.
Strong programming skills in languages such as Python, Java, or Scala, with experience in developing AI and machine learning models in the financial domain.
In-depth knowledge of AI and machine learning concepts and techniques, particularly as applied to financial services, including risk modeling, fraud detection, algorithmic trading, or credit scoring.
Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch, sci-kit-learn) and familiarity with AI model development workflows within financial services.
Proficiency in SQL and hands-on experience with relational databases (e.g., MySQL, PostgreSQL), specifically in the context of financial data management and analytics.
Familiarity with cloud platforms such as AWS, Azure, or GCP, and experience with related AI and machine learning services (e.g., AWS SageMaker, Azure ML) within the financial industry.
Strong understanding of data modeling and data warehousing concepts, with a focus on financial data structures and reporting requirements.
Experience with big data technologies (e.g., Hadoop, Spark) and NoSQL databases (e.g., MongoDB, Cassandra) is a plus.
Excellent analytical and problem-solving skills, with the ability to work with large and complex financial datasets.
Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
Attention to detail and a commitment to delivering high-quality.
