Data Architect AI/ML

Role & responsibilities

• Design and develop scalable data architecture, data pipelines, and AI/ML solutions for large-scale payment and financial datasets.
• Analyze complex datasets to identify business trends, patterns, anomalies, and actionable insights.
• Develop, train, validate, and deploy machine learning models for predictive analytics, classification, regression, clustering, and other business use cases.
• Work closely with Product, Engineering, Business, and Data teams to understand requirements and translate business problems into data and AI/ML solutions.
• Design and implement efficient ETL/ELT pipelines for data ingestion, transformation, validation, and processing.
• Perform data cleaning, preprocessing, feature engineering, and quality checks to ensure reliable model inputs.
• Build and optimize data solutions using AWS services such as Glue, Redshift, and DMR, along with other relevant cloud technologies.
• Work with large datasets using SQL, Python, Pandas, NumPy, SciPy, and Spark.
• Develop meaningful dashboards and visualizations using tools such as Tableau, Power BI, or Superset to communicate insights to business stakeholders.
• Monitor model performance, data quality, and production workflows, and continuously improve accuracy, scalability, and reliability.
• Collaborate with engineering teams on model deployment, integration, version control, and productionization of AI/ML solutions.
• Apply appropriate machine learning and statistical techniques to solve business and financial-data problems.
• Stay current with developments in AI, machine learning, deep learning, cloud data platforms, and data engineering.
• Ensure solutions follow appropriate standards for data security, governance, quality, and maintainability.
• Prepare technical documentation and communicate complex technical findings clearly to non-technical stakeholders and senior management.
• Take ownership of deliverables, manage multiple priorities, and work effectively in a fast-paced, cross-functional environment.

Preferred candidate profile

We are looking for a senior Data Architect / AI-ML professional with 1015 years of hands-on experience across data architecture, data science, machine learning, data engineering, or related areas.

The ideal candidate should have:

• 10–15 years of overall experience in Data Science, AI/ML, Data Engineering, Data Architecture, or a closely related domain.
• Strong hands-on experience working with large-scale datasets, preferably financial, banking, fintech, or payment data.
• Strong programming skills in Python; experience with R is an added advantage.
• Strong understanding of machine learning algorithms, including regression, classification, clustering, predictive modeling, and model evaluation.
• Excellent knowledge of SQL and experience working with relational/database systems.
• Hands-on experience with AWS data services, particularly AWS Glue and Redshift; experience with DMR is desirable.
• Good understanding of data architecture, ETL/ELT, data pipelines, data modeling, and data quality.
• Experience with Pandas, NumPy, SciPy, and preferably PySpark/Spark.
• Experience with data visualization tools such as Tableau, Power BI, or Superset.
• Exposure to TensorFlow, PyTorch, or other deep learning frameworks is preferred.
• Working knowledge of Git/version control, Jupyter Notebook, and modern development practices.
• Experience taking ML models from data preparation through model development, validation, monitoring, and production support.
• Strong analytical and problem-solving capabilities with the ability to convert ambiguous business requirements into practical technical solutions.
• Excellent communication and stakeholder-management skills, with the ability to explain complex data/AI concepts to business and technical audiences.
• Strong ownership, leadership, collaboration, and decision-making skills.
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related discipline.