Top AI & Machine Learning Jobs

Lead design and deliver AI-enabled data platform capabilities (metadata, lineage, data quality, retention). Drive adoption of enterprise-authorized AI-assisted engineering, build agentic production systems, develop secure production code, automate remediation, establish production SRE controls, and mentor teams on responsible AI and validation standards.

Lead a team to design, build, and deliver secure, scalable AI-enabled full-stack solutions. Mentor engineers, drive architecture, implement data-intensive Java/Scala systems, deploy on cloud/Kubernetes, and integrate generative AI (RAG, LangChain/LlamaIndex) with robust testing and CI/CD.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

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.

Leads end-to-end applied AI and machine learning solutions for payment trust, safety, fraud prevention, and operational efficiency. Defines AI/ML strategy, data and evaluation plans, production deployment, model lifecycle practices, monitoring, and incident response. Partners with product, engineering, risk, compliance, and senior stakeholders while remaining hands-on with ML, NLP, document extraction, and cloud-based systems. Drives technical reviews, communicates tradeoffs, and ensures reliable, explainable, audit-ready solutions.

Develop and deploy AI/ML models, data pipelines, and secure production systems. Build scalable architecture for training and inference, transform large datasets, implement NLP and generative AI solutions, and productionize models with data scientists. The role requires Python, Java, ML frameworks, cloud platforms, CI/CD, MLOps, containerization, and responsible AI practices, while contributing to technical communities through demonstrations, presentations, and knowledge sharing.

Lead hands-on development of data infrastructure and retrieval systems supporting agentic AI applications. Build pipelines, APIs, indexing layers, orchestration, memory, and state-management capabilities across enterprise data. Write production Python and PySpark code using Databricks and AWS, enforce data quality, security, compliance, testing, and observability standards, and mentor engineers. Collaborate with product, data science, and business teams while evaluating agentic AI frameworks and responsible AI practices.

Leads data science and analytics initiatives for the credit card business. Translates business questions into analytical solutions, develops statistical and machine learning models, explores large datasets, applies Generative AI, and communicates insights to technical and business stakeholders. Partners across the organization to establish analytical strategies, standards, and scalable products while delivering measurable customer and business impact.

Leads the design and development of scalable software frameworks and secure production code. Architects enterprise AI-enabled engineering workflows, establishes responsible AI guardrails, and improves automation, testing, release readiness, and incident response. Advises cross-functional teams, influences senior stakeholders, defines target-state AI architectures, and guides roadmaps and solution architectures across Consumer and Community Banking.

Leads IAM’s transition to AI-native operations by identifying opportunities, delivering applied AI initiatives, establishing governance and measurement practices, coaching practitioners, and advising executives. The role oversees multidisciplinary transformation efforts involving generative AI, agentic systems, workflow automation, human-agent interaction, adoption, risk management, and benefits realization across a complex, regulated organization.