AI Enablement/Orchestration Engineer - Senior Associate
State Street CorporationMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessStrong employer brand and metro location but niche ML/payments specialization reduces broad applicant density.
High regulatory and payments-domain constraints make cross-industry transferability limited despite transferable ML and MLOps skills.
Multiple mandatory technical and domain requirements including 6+ years, MLOps, RAG, vector DB, and payments expertise.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and operationalize AI/ML anomaly detection and fraud detection models for high-volume payment flows using varied machine learning techniques.
Build scalable batch, streaming, and real-time data pipelines supporting payment monitoring, alerting, and investigation workflows with integration to payment applications and human review processes.
Implement AI orchestration including Gen AI, RAG, and agentic AI for enhanced alert summarization, root cause analysis, governance, and production monitoring in a regulated financial services environment.
Minimum Requirements
6+ years of professional experience; Bachelor’s or Master’s in Computer Science, AI/ML or related field.
3–5 years experience in Data Engineering, AI Engineering, ML Engineering, Software Engineering, or related disciplines with strong Python and SQL proficiency.
Experience with building large-scale data pipelines, distributed data processing (e.g., Kafka, Spark, Airflow), MLOps, and CI/CD practices.
Knowledge and hands-on experience with Generative AI, RAG, OpenAI Service, prompt engineering, vector databases, Docker, Kubernetes, and cloud-native architectures.
Ideal Candidate Profile
Experienced in payments domain with knowledge of payment standards/rails such as SWIFT, ISO 20022, ACH, SEPA, real-time and cross-border payments.
Proficient in developing explainable AI solutions and working with multidisciplinary teams including risk, compliance, cybersecurity, and operations to deploy AI in a highly regulated environment.
Skilled at translating complex business scenarios into operational AI models and services with a focus on anomaly detection, alert quality, operational usability, and regulatory governance.