Top Artificial Intelligence Jobs

Lead development and productionization of AI/ML solutions—focusing on LLMs and NLP—to build interactive coaching tools. Design experiments, tune models, convert prototypes into production code (Python), and integrate generative AI into ML platforms while partnering with product and business teams.

Lead development and deployment of AI/ML models for branch network and field workforce optimization using geospatial, graph, reinforcement learning, and behavioral techniques. Manage end-to-end projects, partner with business, governance, UX, and technology, translate model outputs for stakeholders, and ensure regulatory and model governance compliance.

Lead AI enablement programs within CCB Risk by partnering with stakeholders to define problems, prioritize solutions, build roadmaps, manage delivery, governance, and scale GenAI adoption across the organization.

Provide independent second-line AI compliance, conduct, and operational risk oversight for the CDAO. Assess governance, controls, change management, regulatory readiness (e.g., EU AI Act, NIST), and remediation across firmwide AI initiatives. Challenge AI strategy, monitor integrations and guardrails, support regulatory engagement, and drive risk culture and issue remediation quality.

Conduct applied AI research to develop scalable ML models and algorithms for commercial financial services use cases. Design experiments, analyze data, prototype solutions, collaborate with engineers and stakeholders, contribute to open-source, publications, and patents, and translate research into production-ready applications.

Own end-to-end product for LLM-powered, agentic systems: define requirements and roadmaps, lead pilots to production, build integrations and infrastructure, run model validation and drift detection, monitor performance and safety, manage delivery and stakeholder alignment, and ensure compliance and governance in a regulated banking environment.

Conduct applied AI research to develop novel algorithms and scalable ML models for commercial business problems. Collaborate with data scientists, engineers, and stakeholders to design experiments, prototype solutions, contribute to open source and patents, and translate research into production-ready applications for the financial domain.

Lead development and delivery of enterprise-scale AI/ML and GenAI solutions for home lending. Architect and implement cloud-native MLOps/LLMOps pipelines, deploy LLMs and RAG systems, build AI agents/chatbots, monitor model performance, and collaborate with cross-functional teams to integrate models into production.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead end-to-end development and deployment of ML/AI models for consumer finance products, partner with business and governance, ensure regulatory compliance, monitor performance, and mentor junior team members.

Lead design, development, and deployment of scalable ML and agentic LLM systems for payments. Build data pipelines, run experiments, ensure regulatory and risk compliance, translate model outcomes into business metrics, and collaborate with cross-functional teams to drive AI solutions for fraud detection, payments optimization, and customer experience.

Lead design, build, and deploy production-scale generative AI and ML solutions. Define semantic modeling strategy and unified semantic layer, create ontologies, integrate semantics across pipelines and APIs, enable ontology-driven intelligent workflows, and implement model training, monitoring, governance, and responsible AI practices while partnering with stakeholders.

Design, build, and deploy AI/ML-driven software solutions and data pipelines for LLM/NLP use cases. Implement secure, production-grade code, integrate models via APIs, employ AI-assisted dev tools, and produce architecture, visualizations, and resilient CI/CD-enabled systems while improving code quality and automation.

Lead design and deliver secure, high-quality full‑stack Java applications. Drive adoption of enterprise AI-assisted engineering and agentic workflows, build CI/CD pipelines, mentor teams, evaluate vendors, automate remediation, and promote monitoring and reuse of effective patterns.

Lead design and deliver multi-cloud AI/ML solutions that ingest and correlate telemetry (metrics, logs, traces), build predictive anomaly detection and forecasting models, automate closed-loop remediation, integrate with ticketing systems, and provide self-service or conversational interfaces to reduce MTTR and ticket volume.

Lead strategy and execution for C360 Data Science Center of Excellence, driving AI/ML and modeling initiatives. Collaborate with Risk, Finance, Technology, and CDAO; manage global analytical teams; create executive-level presentations; align stakeholders and advance data-driven solutions including LLMs, semantic search and RAG for financial forecasting and insights.

Lead design and delivery of scalable, secure AI/ML and cloud-based applications. Build reusable frameworks, produce production-quality code, advise cross-functional teams, mitigate technical risk, and influence senior stakeholders to drive platform and product outcomes.

Lead firmwide AI transformation by defining portfolio strategy, prioritizing high-impact AI initiatives, building governance and measurement frameworks, advising senior leaders with data-driven narratives, clearing execution roadblocks, and managing a team to deliver measurable strategic and financial value.

Conduct novel AI/ML research and develop algorithms, benchmarks, and production-quality code. Collaborate with product and engineering teams to deliver scalable solutions across banking businesses, publish findings, and provide thought leadership.

Lead development of state-of-the-art ML and deep learning solutions for NLP, speech analytics, time series, reinforcement learning and recommendation systems. Collaborate with business, product, legal, compliance and engineering teams to deploy scalable models into production, design experiments and metrics, and coach AI/ML team members.

Lead end-to-end delivery of document extraction and NLP solutions for payments: design scalable model pipelines, develop extraction/classification models, define evaluation and lifecycle practices, ensure compliance and explainability, and partner with cross-functional stakeholders to productionize and monitor ML systems.

Lead full‑stack development of scalable, secure services and UI using Java, Python, and React. Build AI/agentic capabilities with Smart SDK, design data/search solutions (PostgreSQL, OpenSearch), drive cloud-native engineering on AWS with Terraform, improve SDLC via AI-assisted tooling, mentor engineers, and deliver operational reliability.

Lead design, development, and production deployment of GenAI, LLM, and ML solutions. Drive MLOps, platform integration, model governance, performance tuning, and mentor AI engineers while collaborating with product and engineering stakeholders.

Operate and improve a secure employee-facing generative AI platform (LLM Suite). Coordinate releases, intake, billing, access, audit readiness, and governance; manage workstreams, track actions, troubleshoot access, produce reports, and support risk/control inquiries to ensure reliable, compliant platform operations.

Lead development and architecture for scalable, highly available microservices platforms. Write and review production code, drive monolith-to-microservices transformations, adopt AI-assisted engineering practices, evaluate vendor solutions, and promote secure, automated SDLC and operational stability.