Description

Key Responsibilities:

• Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications.
• Fine-tune or adapt open-source and proprietary models Build high-performance models for NLP, document intelligence, anomaly detection, risk scoring, predictive analytics, and decisioning use cases.
• Lead experimentation to evaluate model accuracy, scalability, and fairness. Partner with engineering teams to deploy models on cloud-based ML pipelines (Azure, AWS) & data platforms (Databricks & Snowflake) Work with large-scale structured and unstructured datasets across the payments and financial ecosystem.
• Implement model monitoring, drift detection, and continuous retraining strategies. Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research.
• Drive POCs and innovation initiatives that enhance AI capabilities and differentiate our products.

Required Qualifications:

• Bachelors or Masters in Computer Science, Data Science, Machine Learning, AI, or related field.
• 5+ years of hands-on experience building and deploying machine learning models in production.
• Proven expertise with LLMs, transformer architectures, transfer learning, and model fine-tuning.
• Strong proficiency in Python, PyTorch or TensorFlow, and ML libraries such as Transformers.
• Experience with cloud ML platforms, containerization (Docker/Kubernetes), and MLOps tools.
• Solid understanding of statistical modeling, optimization, and evaluation methodologies.

Preferred Qualifications:

• Experience working in fintech, payments, banking, or fraud/risk environments.
• Background in vector databases, RAG pipelines, and knowledge graph integration.
• Experience with data privacy, model governance, and Responsible AI frameworks.
• Contributions to open-source AI/ML communities or research publications

Key Skills

Education

Bachelor's or Master's degrees