About the Role :

We are hiring a Principal AI Scientist for a tier-1, US-headquartered digital banking SaaS organization that powers mission-critical platforms for leading banks and credit unions globally. This is a highly impactful Applied AI and Research-oriented role for an AI practitioner who can translate emerging advances in Artificial Intelligence, Generative AI, autonomous agents, and machine learning into production-grade capabilities for enterprise financial services.

Key Responsibilities :

AI Research & Innovation:

- Lead research and experimentation across AI, Machine Learning, Generative AI, Agentic AI, NLP, and Deep Learning.

- Evaluate emerging AI models, architectures, techniques, and frameworks for enterprise use cases.

- Design and conduct experiments to validate new AI approaches and translate successful research into production solutions.

- Identify opportunities to apply advanced AI techniques to complex financial services problems.

- Contribute to the organization's AI strategy, technical direction, and innovation roadmap.

Autonomous AI & Decision Intelligence:

- Architect and develop autonomous AI agents capable of reasoning, planning, decision-making, and interacting with enterprise systems.

- Build intelligent decisioning capabilities for complex, real-time business scenarios.

- Develop AI-driven solutions for fraud detection, personalization, risk intelligence, customer engagement, and automated decisioning.

- Design AI systems that combine predictive models, LLMs, business rules, and real-time signals.

- Develop intelligent orchestration frameworks for multi-step AI-driven processes.

Advanced Machine Learning & Modelling:

- Design, train, fine-tune, and optimize advanced ML, Deep Learning, NLP, and Transformer-based models.

- Work with large-scale structured and unstructured datasets to develop predictive and prescriptive models.

- Apply advanced modelling techniques to improve prediction accuracy, decision quality, and system performance.

- Develop approaches for model evaluation, experimentation, interpretability, and continuous improvement.

- Optimize models for production environments with high-volume and low-latency requirements.

GenAI & LLM Research:

- Research and implement advanced LLM and Generative AI architectures for enterprise applications.

- Develop sophisticated prompt frameworks and model adaptation strategies.

- Work on LLM fine-tuning, transfer learning, embeddings, Transformers, and Deep Learning.

- Evaluate foundation models based on accuracy, latency, scalability, reliability, and business suitability.

- Explore the application of GenAI to conversational intelligence, financial insights, automation, and decision support.

Real-Time AI Systems:

- Architect AI-powered real-time decisioning systems capable of processing high-volume data and generating low-latency outcomes.

- Develop predictive intelligence for mission-critical financial services workflows.

- Design scalable model-serving architectures for production environments.

- Collaborate with engineering teams to ensure AI models can operate reliably at enterprise scale.

AI Platform & Productionization:

- Partner with engineering teams to transition AI research and prototypes into robust production systems.

- Design scalable AI/ML architectures using GCP.

- Establish production-grade model deployment, monitoring, validation, and lifecycle management practices.

- Build and optimize MLOps and CI/CD pipelines for AI/ML systems.

- Support high-throughput AI services and model APIs using technologies such as FastAPI.

- Work with Vector Databases and distributed AI infrastructure where required.

Digital Banking & Open Banking:

- Apply AI to digital banking use cases across customer experience, fraud, personalization, risk, and financial intelligence.

- Explore Open Banking use cases involving financial data aggregation, APIs, data-sharing frameworks, and intelligent decisioning.

- Integrate third-party AI capabilities and external data sources into core banking product workflows.

- Work with domain and product teams to identify high-value AI opportunities across banking platforms.

Responsible AI & Model Governance:

- Establish strong standards for model governance, explainability, transparency, and responsible AI.

- Evaluate AI systems for reliability, bias, robustness, and appropriate business behaviour.

- Contribute to frameworks for model validation, monitoring, and risk management.

- Ensure AI solutions meet enterprise security, compliance, and governance expectations.

Required Skills:

- 6+ years of hands-on experience in AI/ML, Data Science, Applied AI, Machine Learning Research, or related areas.

- Strong experience designing and implementing AI/ML models for real-world production use cases.

- Deep understanding of Generative AI, LLMs, Transformers, Deep Learning, and NLP.

- Proven experience building autonomous AI agents and intelligent decisioning systems.

- Strong Python programming and software engineering capabilities.

- Hands-on experience with LLM fine-tuning, prompt frameworks, transfer learning, and model optimization.

- Strong experience with real-time or large-scale AI/ML systems.

- Experience with GCP and production AI/ML deployments.

- Strong understanding of MLOps, CI/CD, model lifecycle management, and model monitoring.

- Experience with FastAPI, Vector Databases, and scalable model-serving architectures.

- Strong research, experimentation, analytical, and problem-solving skills.

- Experience translating AI research into production-ready enterprise solutions.

Preferred Experience:

- Experience in FinTech, Banking, Digital Banking, or Financial Services.

- Experience working with enterprise SaaS or product-based organizations.

- Experience with fraud detection, personalization, risk modelling, conversational AI, or financial decisioning.

- Exposure to Open Banking APIs, data-sharing frameworks, and financial data aggregation.

- Experience with responsible AI, model governance, explainability, and AI risk management.

- Experience working with large-scale production AI platforms.

- Research publications, patents, technical contributions, or demonstrated innovation in AI/ML will be an advantage.