Top Artificial Intelligence Jobs

Lead development and architecture of agentic GenAI solutions for banking use cases; deliver multi-agent systems, build reusable AI services, mentor AI engineers, ensure production-grade scalability, reliability, and security, and collaborate firmwide to scale solutions.

Designs, develops, debugs, and operates AWS-based ETL and AI-enabled data platform services. Produces secure, high-quality production code; creates architecture artifacts; monitors production performance; leverages AI-assisted development tools responsibly; analyzes large datasets to improve systems and drive reliability.

Lead development and maintenance of enterprise corporate websites, building accessible responsive components and integrations using HTML/CSS/JavaScript, AEM/CMS, REST APIs, Node.js, and CI tooling. Apply AI (prompt engineering, assistants, agentic workflows) to accelerate development, testing, and deployment while optimizing performance, SEO, accessibility, and operational reliability.

Lead design, build, and operate production-grade ML and generative AI systems. Drive architecture and MLOps, fine-tune LLMs for NLP use cases, implement monitoring, mentor engineers, partner with product and data science, and communicate results to technical and non-technical stakeholders.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Drive AI adoption in Payments Sales by translating sales priorities into GenAI strategy, product requirements, and data initiatives. Partner with sales, data, product, and platform teams to deliver AI-enabled tools, prioritize use cases, monitor market trends, ensure governance, and support training, adoption, and stakeholder communications to improve sales productivity and client engagement.

Lead and architect production-grade trading systems for Credit Pre-Trade Technology, build AI-native platforms, develop and maintain secure scalable code, partner with trading and product teams, drive AI-assisted engineering practices, support production stability and automation, and lead technical evaluations and communities of practice.

Lead design and delivery of agentic AI and ML-driven solutions integrated into Java-based MarTech platforms. Architect scalable, secure microservices, implement LLM-based automation for lead workflows, build reusable frameworks, mentor teams, and ensure operational stability across cloud, container, and streaming infrastructures.

Lead hands-on development and deployment of ML and LLM-powered solutions: design scalable data pipelines, build and productionize models and inference services, collaborate across global AI, product, and business teams, and deploy solutions to public cloud environments.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.

Lead design and build of scalable, secure AI/LLM-based production systems and frameworks; develop reusable tooling, evaluation and quality mechanisms; review code; guide cross-functional teams; design multi-step AI workflows and integrations; present technical recommendations to senior leaders.

Lead definition and delivery of AI data readiness: set semantic/context standards, improve metadata/lineage/data quality, build prototypes and production conversational analytics, remediate NLQ failures, partner with data product teams, and report progress via scorecards and executive updates.

Lead strategy and execution for accessibility tooling and AI enablement across the firm. Own product roadmap, embed assurance into the PDLC, define guardrails for AI-generated UIs, measure outcomes, and partner with standards, governance, product, design, and engineering to ensure accessible experiences.

Lead and mentor a team to design, build, and operate agentic LLM systems and production LLM-backed APIs. Own end-to-end ML lifecycle, MLOps, inference optimization, vector memory, safety/guardrails, and cross-functional governance for scalable, secure AI deployments.

Lead technical contributor on an agile Investment Banking engineering team. Design, develop, debug, and optimize secure production Java/J2EE/Spring Boot systems; drive adoption and validation of enterprise-approved AI-assisted engineering practices; lead vendor evaluations, automation and remediation efforts, and communities of practice; mentor engineers on responsible AI use and ensure test automation, performance tuning, and operational stability.

Lead strategy and delivery of a Databricks-based model experimentation platform for scalable, secure, compliant AI/ML. Define product vision and roadmap, drive migrations and vendor selection, embed governance, champion observability and operational resilience, and coordinate cross-functional teams to deliver enterprise-grade ML workflows for banking stakeholders.

Lead AI strategy for Payments Sales by translating sales priorities into scalable GenAI and data-driven products, driving adoption, partnering with product/data teams, ensuring governance, and enabling sales through training and cross-functional coordination to improve productivity and revenue outcomes.

Lead end-to-end execution and governance of AI pilots across the CMO organization. Coordinate onboarding, approvals, and tracking of LOB POCs to avoid duplication; manage the Marketing AI Sandbox; scout external AI solutions and partnerships; and communicate program status to stakeholders while measuring program success and alignment with enterprise priorities.

Lead oversight of AI/ML and cybersecurity operational risk across the firm: evaluate technology and cyber practices, identify threats, challenge first-line mitigations, conduct thematic analyses, assess AI/ML use cases, and collaborate with stakeholders to strengthen controls and ensure responsible AI adoption.

Lead product vision and multi-year roadmap for a real-time fraud AI/ML platform. Drive development of transformer and graph-based models, feature infrastructure, MLOps, experimentation, governance, and cross-functional delivery to reduce fraud loss while preserving customer experience.

Lead identification, development, piloting, and scaling of AI use cases for channel strategy and optimization. Partner with analytics, product, and controls teams to integrate AI, establish ROI metrics, build prompt libraries, and drive adoption across marketing channels while documenting best practices.

Lead research and production deployment of LLM-based agent systems for multi-step workflows. Define research directions, fine-tune and optimize models for latency and cost, apply RL/preference optimization, ensure safety/compliance (auditability, PCI-related controls), scale systems (caching, batching, evaluation), design experiments and metrics, and partner with Product/Engineering/Risk to ship human-centered, explainable AI features at scale.

Lead design and delivery of production-grade ML and generative AI solutions for Markets Operations. Set technical direction, deploy and monitor models, manage a team, ensure reliability and governance, partner with stakeholders to prioritize and measure impact.

Lead product strategy and roadmap for agentic AI and LLM-driven automation in corporate and investment bank operations. Drive discovery, define PRDs and success metrics, manage backlog, coordinate cross-functional teams, evaluate AI impact on business metrics, and deliver scalable production launches.