AI Enablement/Orchestration Engineer - Senior Associate

Job Description:  AI Enablement/Orchestration Engineer Role Summary & Role Description Our technology function, Global Technology Services (GTS), is vital to State Street and is the key enabler for our business to deliver data and insights to our clients. We’re driving the company’s digital transformation and expanding business capabilities using industry best practices and advanced technologies such as cloud, artificial intelligence and robotics process automation. We offer a collaborative environment where technology skills and innovation are valued in a global organization. We’re looking for top technical talent to join our team and deliver creative technology solutions that help us become an end-to-end, next-generation financial services company. Join us if you want to grow your technical skills, solve real problems and make your mark on our industry. We are seeking a motivated and enthusiastic AI Enablement Engineer to join our team and contribute to the development and integration of cutting-edge AI solutions into our business processes. This is a highly technical and hands-on role where you will work closely with senior team members to build AI systems leveraging technologies such as Generative AI (Gen AI), Retrieval-Augmented Generation (RAG), Agentic AI frameworks. You will collaborate closely with software engineers and investment servicing business teams to solve complex business challenges in the payments domain. This role requires strong capabilities in both AI Engineering and Data Engineering. You will design data products, orchestrate multi-agent workflows, develop Retrieval-Augmented Generation (RAG) systems, integrate enterprise knowledge sources, and establish the governance and observability capabilities required to operate AI safely within a highly regulated financial services environment. State Street is accelerating the adoption of AI-enabled capabilities to improve operational efficiency, enhance cybersecurity resilience, strengthen risk management, and deliver intelligent experiences across the enterprise. As an AI Orchestration Engineer, you will help establish the AI execution layer that enables secure collaboration between enterprise data platforms, large language models, internal knowledge repositories, agentic workflows, governance controls, and human decision makers. What You Will Be Responsible For Design, build, and operationalize AI/ML solutions to detect anomalies, exceptions, fraud indicators, operational risk signals, and process deviations across high-volume payment flows. Develop anomaly detection models using supervised, unsupervised, semi-supervised, time-series, graph-based, statistical, and hybrid machine learning approaches. Engineer features from payment transactions, exception events, operational logs, historical patterns, and reference data to identify unusual value, volume, velocity, counterparty, timing, currency, geography, and repair/reject behaviors. Build scalable batch, streaming, and near-real-time data pipelines to support payment monitoring, detection, scoring, alert generation, and downstream investigation workflows. Tune detection thresholds, scoring logic, and model sensitivity to balance early detection, precision, recall, false positive reduction, and operational usability. Develop explainability capabilities, reason codes, model diagnostics, and investigation summaries to help payment operations, risk, and technology teams understand why anomalies are flagged. Integrate anomaly detection services with payment applications, case management tools, workflow platforms, APIs, data stores, and human-in-the-loop review processes. Use GenAI, RAG, and agentic AI capabilities selectively to support alert summarization, root-cause analysis, knowledge retrieval, operational playbooks, and assisted triage. Implement MLOps practices for experiment tracking, model versioning, deployment automation, reproducibility, monitoring, rollback, and production support. Monitor model performance, alert quality, drift, data quality, investigation outcomes, SLA adherence, and production reliability across anomaly detection use cases. Partner with payments operations, product, risk, compliance, architecture, cybersecurity, and engineering teams to translate business scenarios into detection patterns and AI-enabled controls. Apply Responsible AI, model risk management, auditability, data governance, security, and regulatory control requirements for AI solutions in a financial services environment. Collaborate with cross-functional teams to identify AI use cases and translate business requirements into agent-based solutions. Education & Qualifications Skills Required Hands-on experience building anomaly detection, fraud detection, transaction monitoring, risk scoring, or payment intelligence models using supervised, unsupervised, semi-supervised, and hybrid approaches. Strong understanding of anomaly detection techniques such as clustering, isolation forests, one-class SVM, autoencoders, gradient boosting, time-series anomaly detection, graph-based detection, and statistical outlier methods. Ability to engineer features from high-volume payment data, including amount, currency, value date, settlement date, counterparty, account, geography, payment type, frequency, velocity, historical behavior, exception codes, and repair/reject patterns. Experience tuning detection thresholds and model sensitivity to optimize precision, recall, F1 score, alert quality, false positive reduction, and operational triage effectiveness. Knowledge of payment flows, payment exceptions, duplicate payments, unusual value or volume patterns, payment repairs, rejects, sanctions referrals, cut-off breaches, and operational risk indicators. Experience deploying batch, streaming, or near-real-time inference pipelines using technologies such as Kafka, Spark, Airflow, cloud data platforms, APIs, and MLOps tooling. Ability to implement explainability and investigation support using SHAP, LIME, reason codes, model diagnostics, rule-model hybrids, and GenAI-assisted alert summarization. Experience monitoring model drift, data quality, alert trends, investigation outcomes, SLA performance, and production reliability for anomaly detection systems. Minimum Qualifications 6+ years of professional experience; Bachelor's or Master's in Computer Science, AI/ML or related field. 3–5 years of experience in Data Engineering, AI Engineering, Machine Learning Engineering, Software Engineering, or related disciplines. Strong experience developing large-scale data pipelines and distributed data-processing solutions. Proficiency in Python with solid software engineering fundamentals Experience with SQL, APIs, workflow automation, and cloud-native architectures. Solid understanding of RAG, experience with OpenAI Service and prompt engineering techniques. Knowledge of vector databases. Experience integrating LLMs with external databases, APIs and storage systems Experience in CI/CD-based projects. Knowledge of Docker and Kubernetes for deploying AI services Strong communication and stakeholder collaboration skills. Experience using Jira as an agile management tool. Preferred Qualifications Experience in Payments domain. Familiarity with payment standards and rails such as SWIFT, ISO 20022, wires, ACH, SEPA, real-time payments, and cross-border payment processing is preferred. Experience in IBM MQ,  Kafka, MFT. Knowledge of Responsible AI, data governance, and model risk management. Work Schedule Hybrid Keywords (If any) Why this role is important to us Our technology function, Global Technology Services (GTS), is vital to State Street and is the key enabler for our business to deliver data and insights to our clients. We’re driving the company’s digital transformation and expanding business capabilities using industry best practices and advanced technologies such as cloud, artificial intelligence and robotics process