Payment Hub _ AI engineer _ India _ 5+ Yrs.,_ 1 Position

About the role:

As an AI/ML hands on engineer, you will be responsible for developing and deploying AI and machine learning models tailored to the financial domain. This includes risk management, fraud detection, credit scoring, predictive analytics, and process automation. You will work closely with data scientists, engineers, and business stakeholders to create scalable and secure AI/ML architectures that align with business objectives and compliance standards.

What you will be doing:

Design and develop end-to-end AI/ML product tailored for financial use cases such as fraud detection, credit risk assessment, portfolio optimization, and predictive analytics.

Implement machine learning pipelines that support both real-time and batch processing requirements.

Develop strategies for model retraining and continuous improvement based on real-world financial data and outcomes.

Model Development and Deployment:

Develop, train, test, and deploy AI models.

Ensure that models are integrated into the organization's production environment, following best practices for scalability and maintainability.

Optimize models for performance, accuracy, and interpretability in the financial context.

Data Pipeline Management:

Collaborate with data engineers to build data pipelines that support AI/ML model development, ensuring data integrity, privacy, and compliance

with financial regulations (e.g., GDPR, PCI DSS).

Design solutions to process large-scale datasets, both structured and unstructured, using distributed computing platforms (e.g., Spark, Hadoop,

or cloud-based solutions like AWS, GCP, Azure).

Collaboration and Stakeholder Management:

Work with cross-functional teams including product, engineering, and operations to identify business challenges that can be addressed using AI/ML.

Communicate complex technical concepts to non-technical stakeholders, ensuring alignment with business goals and risk management.

Governance and Compliance:

Ensure that solution meet the regulatory and compliance requirements of the financial industry.

Develop governance frameworks around model interpretability, fairness, and transparency, adhering to legal and ethical standards. Implement strong security measures to protect financial data and AI assets.

What you will need:

Hands on experience in AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or H2O.ai.

Deep understanding of model optimization techniques and machine learning algorithms like XGBoost, Random Forest, and deep learning architectures

(RNN, CNN, LSTM).

Strong programming skills in Python, R, Java, or Scala.

Experience with SQL and NoSQL databases.

Familiarity with data privacy and security regulations in the financial domain (e.g., GDPR, PCI DSS, SOX).

Experience in working with cloud technologies.

Experience with Natural Language Processing (NLP), LLM.

Familiarity with Explainable AI (XAI) techniques to ensure transparency and accountability in decision-making processes.