Top AI & Machine Learning Jobs

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 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.

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 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.

Senior analytics leader partnering with business and finance to deliver trusted metrics, improve data quality, govern portfolio metrics, and generate decision-ready insights. Leads end-to-end analytics, drives automation and self-service, validates AI use, and communicates executive-level findings to manage lending performance, risk, and operational efficiency.

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 development of analytics and AI/ML solutions for the credit card business: translate business problems into analytical approaches, build and evaluate models (including generative AI), code solutions, perform exploratory analysis, communicate insights to stakeholders, and partner across teams to deliver scalable, responsible analytics products.

Lead design and delivery of large-scale recommendation, ranking, and personalization systems. Own end-to-end ML lifecycle, apply NLP/LLM and retrieval signals, use GenAI pragmatically, operationalize evaluation and monitoring, build and mentor applied science and ML engineering teams, and drive low-latency, reliable production ML services and experimentation.

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 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.