Top AI & Machine Learning Jobs

Lead and conduct commercially oriented AI research: formulate problems, develop algorithms and benchmarks, run experiments, produce production-quality Python code, collaborate with engineering and business stakeholders, publish findings, and own strategic research portfolios that drive business impact.

Lead architecture for digital and platform services, design cloud-native, microservices and AI-enabled solutions, integrate GenAI into systems, drive governance, produce secure production code, and influence technical direction across teams.

Lead orchestration and scaling of marketing AI proofs-of-concept across the CMO organization. Manage intake, pilot execution, governance, stakeholder alignment, risk controls, and executive reporting to enable safe, compliant AI innovation and graduation to production.

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.

Lead design and delivery of production-grade LLM and multimodal AI systems. Architect scalable APIs, agentic workflows, and high-throughput microservices. Own end-to-end reliability, performance, and continuous improvement while partnering with ML engineering, product, and cloud teams to deploy secure, enterprise-grade AI at scale.

Lead development and management of agentic AI workbench and control environment, drive integration, deployment, and optimization of agent-led workflows, partner with architecture and engineering, manage risk and KPIs, support stakeholder adoption, and mentor team members to deliver measurable business value across Client360.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

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.