Top AI & Machine Learning Jobs

Supports quantitative model maintenance, optimization, validation, testing, documentation, and implementation. Analyzes model outputs and large datasets, develops automated testing frameworks and migration scripts, performs database and data-quality testing, and applies GenAI, machine learning, and automation to improve workflows. Partners with finance and credit modeling teams, assesses risks and controls, presents recommendations, and collaborates with global stakeholders on platform migration and model reengineering initiatives.

Defines enterprise architecture, engineering standards, governance, and innovation roadmaps for public-cloud data, analytics, AI/ML, and agentic AI platforms. Leads cloud transformation, lakehouse and real-time analytics architecture, AI-agent frameworks, MLOps, governance, security, reliability, automation, and regulatory compliance. Partners with engineering, cybersecurity, risk, product, and business teams to deliver scalable, secure, resilient, and cost-efficient data and AI ecosystems.

Leads enterprise architecture and engineering strategy for public-cloud data, analytics, AI/ML, and Agentic AI platforms. Defines cloud-native architectures, engineering standards, governance, reliability, security, and compliance controls. Modernizes legacy data platforms, scales lakehouse and streaming ecosystems, and enables self-service data products. Designs AI-agent frameworks, automation, MLOps, responsible AI controls, and production governance while partnering with engineering, cybersecurity, risk, compliance, and business stakeholders.

Leads cross-functional teams in identifying, designing, prototyping, and implementing enterprise AI initiatives. Evaluates business challenges, technological readiness, data availability, and implementation resources. Applies generative AI, LLM, RAG, prompt engineering, analytics automation, and related patterns to data and decision-science workflows. Translates business needs into technical requirements, prototypes, testing, and implementation recommendations while supporting responsible AI, governance, security, quality, and risk management.

Designs and accelerates AI-enabled capabilities across ITSM products, initially Incident, Change, and Problem Management. Translates workflows into practical solution architectures, evaluates automation and AI patterns, prototypes capabilities, and guides implementation. Partners with product, engineering, architecture, platform, and governance teams to ensure scalable, supportable, risk-aligned solutions. Coaches teams on AI design tradeoffs, influences strategy, and establishes repeatable patterns and success measures across multiple ITSM offerings.

Designs and accelerates AI-enabled capabilities across ITSM offerings, initially Incident, Change, and Problem Management. Translates workflows into scalable solution designs, evaluates deterministic automation, predictive, generative, agentic AI, and API approaches, and conducts prototypes and experiments. Produces architecture and implementation guidance, coaches product and engineering teams, influences senior stakeholders, and ensures solutions meet enterprise standards for scalability, supportability, risk, and measurable business outcomes.

Designs, prototypes, evaluates, and guides deployment of AI-enabled capabilities across ITSM offerings, especially Incident, Change, and Problem Management. Partners with product, engineering, architecture, platform, and governance teams to select appropriate automation, analytics, machine learning, generative AI, agentic AI, or API-based patterns. Creates solution artifacts, coaches teams, influences strategy, establishes scalable design patterns, and defines success measures while managing operational and risk considerations.

Lead design and delivery of AI-driven front-line monitoring solutions using Python, SQL, and SAS. Develop anomaly detection, feature engineering, visualizations, and agentic LLM workflows to detect conduct and compliance risks. Provide technical ownership for scalable, explainable, auditable analytics, mentor junior staff, and drive SAS-to-Python and GCP-based modernization.

Validate NLP and Generative AI models within Model Risk Management, assessing methodology, data integrity, performance, implementation, and monitoring. Develop test models, analytical reports, validation tools, and evaluation pipelines while managing model risk across the lifecycle. Collaborate with auditors, regulators, model stakeholders, and technology partners on quantitative modeling, governance, automation, and compliance initiatives.

Develop and deploy foundation models, generative AI, agentic systems, and traditional machine learning solutions at enterprise scale. Design multi-agent workflows, RAG applications, training and deployment pipelines, and human-in-the-loop systems. Pre-train, fine-tune, evaluate, monitor, and optimize transformer models using distributed computing and techniques such as distillation, quantization, pruning, LoRA, and PEFT. Collaborate with technical, business, regulatory, and audit stakeholders while supporting responsible AI, governance, risk management, and model performance improvement.