Lead GenAI Java Developer - VP

About the role

The Fraud Technology group within Morgan Stanley's Network Financial Risk & Technology (NFRT) delivers solutions to detect, prevent, and analyze fraud across the enterprise. This role involves leading the evolution of Morgan Stanley's real-time fraud screening platform, integrating machine learning and Generative AI capabilities.

Responsibilities

• Lead design and development of high-performance Java or Scala microservices for real-time fraud detection.
• Architect scalable solutions incorporating LLMs, vector search, prompt engineering, and RAG patterns.
• Integrate GenAI capabilities such as alert explanation, anomaly summarization, synthetic data generation, and automation.
• Drive cloud-ready and containerized development using Docker and Kubernetes.
• Partner with data science teams to productionize machine learning and GenAI models.
• Implement APIs for AI inference, model orchestration, and governance.
• Ensure compliance with responsible AI, model risk, and data privacy standards.
• Guide engineering teams in CI or CD, DevOps tooling, code quality, and observability.
• Mentor junior engineers and promote innovation and continuous learning.
• Collaborate with fraud analysts, reporting teams, and data governance stakeholders.
• Contribute to the target-state architecture for fraud detection platforms.
• Evaluate new AI technologies and frameworks for enterprise adoption.
• Support roadmap planning and long-term strategic decisions.

Requirements

• 10+ years of hands-on Java engineering experience with strong knowledge of performance, concurrency, and distributed systems.
• Experience with Scala or willingness to learn.
• Strong understanding of microservices, distributed caching, and relational databases such as Sybase, Oracle, or MS SQL.
• Knowledge of messaging or middleware such as Kafka and MQ.

Skills

• Practical experience with GenAI technologies including LLMs, prompt engineering, RAG, vector databases, model deployment and inference, and Python for ML/AI workflows.
• Familiarity with MLOps, feature stores, and model monitoring.
• Cloud and DevOps experience building cloud-ready applications and containerized deployments using Docker and Kubernetes.
• Knowledge of CI or CD pipelines, automated testing, and observability tools.
• Strong analytical and problem-solving ability.
• Excellent written and verbal communication skills.
• Ability to work effectively in a global and fast-paced environment.
• Strong stakeholder management and leadership skills.

Preferred Skills

• Background in fraud, cybersecurity, risk technology, or financial services.
• Understanding of reactive programming.
• Experience working in Agile or Scrum environments.
• Hands-on experience with distributed systems, event-driven architectures, and API-first design.