Top Artificial Intelligence Jobs

Lead and contribute to design, develop, and maintain scalable Java/Spring Boot services on AWS. Drive adoption of enterprise-authorized AI-assisted engineering practices, ensure operational stability and monitoring (Prometheus/Grafana), troubleshoot complex issues, participate in architecture and code review, automate remediation, and mentor/lead software engineers.

Lead HR Data & AI controls governance across the HR data and analytics lifecycle. Design and embed pragmatic controls, drive risk assessments, remediation, and audit readiness for HR data products and AI/ML usage. Partner with Data Product, Engineering, legal, compliance and stakeholders to operationalize control frameworks, monitoring, and evidence capture for sensitive HR/EX data.

Lead the design and implementation of AI-enhanced, pixel-perfect React front-end interfaces for large-scale internal tools. Own and maintain code quality, performance, accessibility (WCAG), testing, and CI/CD practices. Collaborate with product, design, and backend teams, mentor junior engineers, and drive responsible adoption of enterprise-authorized AI-assisted development tools.

Lead adoption of agentic AI across product teams by coaching, prototyping, and embedding new AI-enabled workflows. Redesign operating models, translate AI concepts into playbooks and learning paths, measure adoption and impact, and partner with leaders to scale responsible AI practices across product delivery in a large regulated banking environment.

Lead end-to-end causal and predictive ML solutions for private banking: frame causal problems, build uplift/HTE and observational causal studies, deploy models and MLOps, apply LLMs/agentic AI, ensure model quality, collaborate with stakeholders, compliance, and firm AI community.

Design, implement, and operate production-grade agentic AI and LLM-enabled services on AWS. Build retrieval-augmented systems, APIs, and integrations; optimize performance, scalability, cost, and observability; implement evaluation, testing, guardrails, and secure development practices while collaborating across product and engineering teams.

Lead engineering on cloud-based AI/ML data platforms: design and ship secure production code, guide teams on AI-assisted development and SDLC automation, drive MLOps and DAG-based pipelines adoption, coach on responsible AI practices, and influence cross-team tooling and governance.

Lead PMO and enablement for the CAO AI Solutions portfolio: manage intake, prioritization, roadmaps, governance, documentation, reporting, and stakeholder communications to drive disciplined execution and operational rigor across AI initiatives.

Lead development and production of multi-agent AI systems that automate software delivery. Own agent capabilities end-to-end with emphasis on reliability, evaluation, observability, cost efficiency, and operational stability; build production-grade code, evaluation frameworks, and lead cross-functional teams while integrating LLM APIs and agent frameworks on cloud-native infrastructure.

Lead product strategy and roadmap for sales-focused applied AI products. Define user journeys and requirements, drive adoption, ensure compliance and data quality, partner with Data Science/Technology to deliver GenAI insights and agentic AI workflows that automate sales processes.

Own and ship production-grade multi-agent AI systems that automate software delivery pipeline stages. Design, build, and maintain reliable, observable, and cost-effective agent capabilities; implement evaluation and quality mechanisms; lead cross-functional teams; ensure operational stability, clear failure modes, and scalable production code; influence senior stakeholders.

Design, prototype, and productionize agentic LLM systems: frame problems, build datasets, develop models/agents, evaluate and monitor performance, and deploy robust, observable production code while partnering with cross-functional teams.

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 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.

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 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.

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 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 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 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.