Top Artificial Intelligence Jobs

Lead discovery and delivery of applied generative AI and automation for co-branded card products. Define vision, roadmap, and measurable outcomes; translate complex workflows into agent-enabled processes; partner with design, engineering, data, legal, risk, and operations; embed responsible AI controls; track KPIs; drive adoption through change management and continuous improvement.

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 design, build, and operate secure, scalable cloud platforms optimized for AI/ML. Provide technical guidance, develop production code, implement IaC and CI/CD, optimize cloud performance/costs, drive AI-assisted engineering governance, and partner with AI teams to translate compute needs into infrastructure requirements.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Build and operate production-grade multi-agent AI systems that automate software delivery. Own end-to-end agent capabilities focusing on reliability, evaluation, observability, cost-per-session, and operational robustness while leading cross-functional teams and influencing senior stakeholders.

Lead and coach multiple engineering teams to deliver AI/LLM-enabled capabilities across the ProductGPT ecosystem. Provide hands-on technical direction, architecture guidance, and resource decisions; establish LLM integration, prompt/context management, orchestration, evaluation, and safe deployment practices; drive cloud-native AWS solutions, CI/CD, data patterns, and operational excellence while partnering with product and stakeholders.

Lead end-to-end development, deployment, and monitoring of ML and AI models for consumer finance products. Translate business needs into analytical plans, ensure governance and regulatory compliance, advise stakeholders, and mentor junior modelers.

Design, build, and operate agentic LLM systems and RAG pipelines to translate legacy mainframe logic into verified Kotlin/JVM services. Develop ingestion, orchestration, verification, testing, and LLMOps infrastructure, partner with domain SMEs, extend ETL/CDC pipelines, and drive adoption of safe, enterprise AI-assisted engineering practices to deliver reliable, auditable production services.

Lead end-to-end delivery of a domain-specific payments foundation model (LPM), coordinating applied AI/ML, engineering, data pipelines, and governance. Own program plans, dependencies, budgets, integration patterns (real-time, batch, streaming), model evaluation and production readiness, risk/controls, and executive reporting to ensure scalable, compliant downstream adoption.

Develop and operationalize ML and deep learning solutions (especially NLP/LLMs) for Wholesale Lending. Design experiments, build batch and real-time prediction pipelines and end-to-end AutoML/AutoNLP workflows, collaborate with product, data, engineering, legal and compliance teams, and deploy models to production on AWS.

Lead design, build, and maintain scalable image-focused data pipelines and AWS-based infrastructures to ingest, preprocess, OCR, store, and route extracted text and metadata. Deploy and scale containerized image processing services on EKS, integrate OCR outputs with databases and analytics, enforce data governance/security, collaborate with ML teams on dataset/versioning, and lead/mentor data engineering teams to implement CI/CD and best practices.

Lead AI product strategy and execution for large banking operations initiatives. Guide product managers and engineers, write roadmaps and PRDs, collaborate with executive stakeholders, and apply cutting-edge LLM and agentic AI techniques to deliver high-impact solutions.

Lead design, delivery, and production deployment of enterprise AI/ML solutions for wholesale payments operations. Build scalable NLP, document understanding, LLM-enabled applications on AWS, establish MLOps, model governance, monitoring, and responsible AI. Mentor engineers, set engineering standards, and partner with stakeholders to define roadmaps and measurable outcomes.

Lead technical strategy and delivery of a lending platform by embedding AI-native capabilities (agentic workflows, LLM features). Drive multi-team implementations, manage stakeholders, set AI-assisted engineering governance, establish validation standards, and ensure secure, scalable, high-performance cloud-based systems.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead delivery of complex AI programs across Consumer & Community Banking by developing project plans, managing resources, budgets, and risks, validating AI-assisted outputs, collaborating with engineering/product teams, championing agile practices, and presenting to senior stakeholders to ensure alignment with business goals.

Lead research and production deployment of LLM-based agent systems for multi-step financial workflows. Define research directions (planning, tool use, safety), fine-tune and optimize models (PEFT, distillation, quantization), apply RL and preference optimization, scale systems (caching, batching, governance), implement privacy/safety controls (PCI, auditability), design experiments and metrics, and partner cross-functionally to deliver safe, explainable AI at scale.

Lead design, build, and scaling of production-grade NLP and LLM-based solutions across the model lifecycle. Partner with product, engineering, operations, and risk to translate operational needs into intelligent search, summarization, classification, and recommendation systems. Ensure model reliability, security, scalability, data quality, monitoring, and continuous iteration to improve agent efficiency and customer outcomes.

Lead engineer designing, building, and operating AI-focused infrastructure: write secure production code, implement CI/CD for ML, use IaC and automation, manage Kubernetes/Docker, optimize cloud resources, drive adoption of enterprise AI-assisted engineering and responsible AI practices.

Lead technical design and development of secure, scalable Java-based distributed systems; write and review production code; drive automation, observability, and resiliency; lead vendor evaluations and engineering communities; promote and validate enterprise AI-assisted development practices and responsible AI use across the team.

Lead and deliver large-scale applied AI/ML initiatives for wholesale lending: define problem statements, design and productionize state-of-the-art ML/LLM solutions (NLP, personalization, recommendation), build end-to-end pipelines (batch and real-time) on AWS, collaborate with product, engineering, and risk/compliance partners, communicate results to senior stakeholders, and drive team execution and culture.

Lead design, build, and support full-stack solutions (Java/Spring Boot backends, RESTful microservices, React/Angular UIs). Promote AI-assisted engineering, secure/resilient APIs, CI/CD, observability, and production support while collaborating with cross-functional teams.

Design, deploy, and scale production LLM agents for finance workflows using RAG, tool use, and multi-step reasoning. Build data and inference pipelines, integrate agents with APIs and BI, implement evaluation/guardrails, optimize for scale and cost, and apply MLOps practices while partnering with Finance and Engineering to deliver measurable business outcomes.

Lead cybersecurity architecture for AI-enabled products across the CCB portfolio. Advise on strategy, design reusable cloud-native security frameworks, drive adoption of new methods, evaluate emerging technologies, and translate complex technical risks and mitigations to leadership.

Lead the AI use case portfolio for Trade and Working Capital, translating business needs into technical requirements, prioritizing and managing delivery from ideation to value realization, enabling product, commercialization, and productivity AI use cases while driving adoption and governance alignment across stakeholders.