Description
Mastercard is seeking a Data Scientist I to implement generative AI models and applications for Anti-Money Laundering (AML) transaction monitoring use cases. The successful candidate will work on building advanced solutions leveraging Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), tool calling, and LangChain/LangGraph for dynamic workflows.
Responsibilities:
- Implement generative AI models and applications for AML transaction monitoring use cases.
- Build advanced solutions leveraging RAG, MCP, tool calling, and LangChain/LangGraph for dynamic workflows.
- Develop machine learning pipelines for fine-tuning large language models (LLMs) and other foundation models.
- Implement content generation systems for text, image, and multimodal outputs.
- Apply computer vision techniques for image analysis, creative generation, and visual search.
- Implement evaluation frameworks for GenAI applications, including hallucination detection, bias checks, and quality scoring.
- Build observability and monitoring solutions for AI systems, including latency, cost tracking, and model performance metrics.
- Build scalable solutions on cloud platforms (AWS, Azure) and leverage data platforms like Databricks.
- Integrate AI models into production systems with a focus on performance, security, and compliance.
- Stay current with the latest advancements in AI/ML research, particularly in generative AI, and apply them to real-world problems.
- Promote engineering best practices and contribute to a culture of innovation and collaboration.
Requirements:
- Strong knowledge in building and deploying generative AI solutions (e.g., LLMs, diffusion models, transformers).
- Expertise in RAG pipelines, MCP-based integrations, tool calling frameworks, and LangChain/LangGraph.
- Proficiency in Python and popular AI/ML frameworks such as PyTorch or TensorFlow.
- Experience with content generation systems (text, image, multimodal) and computer vision models.
- Exposure to evaluation techniques for GenAI (e.g., hallucination detection, factuality scoring, bias evaluation).
- Knowledge of observability tools for AI systems (e.g., monitoring latency, cost, and performance metrics).
- Exposure to MLOps practices, including model versioning, CI/CD for ML, and monitoring in production.
- Exposure to cloud platforms (AWS, Azure) and data engineering tools like Databricks.
- Solid understanding of prompt engineering, fine-tuning, and model evaluation techniques.
- Knowledge of API development and integration of AI models into web or mobile applications.
- Strong problem-solving skills and ability to work in a fast-paced, high-impact environment.
- Excellent communication and collaboration skills to work effectively with technical and non-technical stakeholders.
Education:
- Bachelor or Master's Degree in Computer Science or equivalent