Lead GenAI Java Developer - VP

Morgan Stanley•New York, NY

1d•$150,000 - $210,000

About The Position

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management, and wealth management services. The Firm's employees serve clients worldwide including corporations, governments, and individuals from more than 1,200 offices in 43 countries. The Fraud Technology group within NFRT delivers solutions to detect, prevent, and analyze fraud across the enterprise. We partner with cybersecurity, fraud analytics, compliance, legal, data governance, and operations teams to design scalable and intelligent fraud detection platforms. Our work includes building real-time, batch, and analytical systems, integrating vendor solutions, and driving adoption of Generative AI across fraud workflows. As a Vice President - Java Engineering and Generative AI, you will lead the evolution of Morgan Stanley's real-time fraud screening platform, integrating machine learning and Generative AI capabilities. You will collaborate with architects, analytics teams, and data governance partners while providing technical leadership to an Agile squad. The role includes shaping engineering best practices, mentoring developers, driving solution design, and championing GenAI adoption across Fraud Technology.

Requirements

10 plus 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.
Practical experience with GenAI technologies including: LLMs such as OpenAI, Azure OpenAI, Anthropic Prompt engineering and RAG Vector databases such as Pinecone, FAISS, Weaviate, Elastic Vector Search Model deployment and inference using tools such as Transformers, LangChain, LlamaIndex
Hands-on experience with Python for ML or AI workflows.
Experience building cloud-ready applications and containerized deployments using Docker and Kubernetes.
Knowledge of CI or CD pipelines, automated testing, and observability tools such as Grafana, Splunk, and Prometheus.
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.

Nice To Haves

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.
Familiarity with MLOps, feature stores, and model monitoring is a plus.

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.