Top Artificial Intelligence Jobs

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, build, and operate production generative AI services and reusable backend APIs. Combine enterprise data with large language and multimodal models, ensure scalability, performance, reliability, and responsible AI controls. Collaborate with ML, cloud, and SRE teams; translate research into production, optimize inference and cost, and drive operational improvements and measurable business outcomes.

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.

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.

Lead design, build, and maintain scalable ML platform infrastructure and tools. Drive adoption of AI-assisted engineering practices, implement secure production code, enable MLOps (CI/CD, model versioning, monitoring), provide technical leadership, and promote governance, reuse, and automation across the SDLC.

Lead an agile engineering team to design, build, and deliver secure, scalable AI-enabled products. Provide technical guidance, write and review production code, drive product and architecture decisions, adopt enterprise AI-assisted development practices, implement RAG and LLM solutions, and ensure responsible AI guardrails and evaluation frameworks.

Lead design, development, and production deployment of document AI solutions (image classification, text categorization, extraction). Architect CV/NLP models (CRNN, transformers), integrate OCR and generative AI, build scalable AWS SageMaker/EKS pipelines, implement MLOps, and lead a team of ML engineers to meet business KPIs.

Lead architecture, development, deployment, and scaling of advanced AI/ML solutions for credit card business. Translate business needs into technical solutions, establish MLOps and governance best practices, validate and monitor models, and build and mentor a high-performing AI/ML team while partnering across Product, Risk, Finance, Technology, and Compliance.

Lead AI-driven product transformation for payments: design future operating models, scale reusable AI platforms, redesign client workflows, drive adoption and responsible AI governance, define KPIs and change programs, and partner across product, engineering, operations, and risk to realize measurable outcomes.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Drive end-to-end model lifecycle: data quality, evaluation, monitoring, and stakeholder collaboration to improve operational workflows and reduce agent effort.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead full-stack engineering efforts to design, build, and operate cloud-native Java/JavaScript/Python applications and scalable data pipelines. Develop and deploy AI/ML models (cloud and edge), integrate streaming systems (Kafka), manage AWS infrastructure, drive AI-assisted development practices, and provide technical leadership across architecture, code quality, and operational excellence.

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Lead design and delivery of scalable, secure production ML and LLM-based systems. Provide technical guidance, build production-quality code, oversee ML architecture, monitoring, MLOps, AI agents and RAG solutions, and advise executives to improve operational workflows and model reliability.

Lead development of AI-powered, cloud-native workforce planning systems using Java, Spring, microservices, and AWS. Drive adoption of AI-assisted engineering, ensure secure, scalable production code, automate remediation, evaluate vendor solutions, and lead engineering communities of practice.

Design, build, and scale NLP and LLM-based solutions across the end-to-end model lifecycle. Partner with product, engineering, operations, and risk to deploy reliable, secure, production-grade systems (search, summarization, classification, recommendations). Monitor models, improve data quality and feedback loops, mitigate ML risks, and communicate technical decisions to stakeholders to reduce agent effort and improve operational outcomes.

Lead a global engineering organization owning case management, workflow/task orchestration, and AI automation platforms. Define strategy, drive hybrid-cloud architecture and platform reliability (high uptime targets), deliver AI-enabled automation and integrations, manage incidents and resiliency, ensure auditability and analytics, partner with Operations/Product, and build high-performing teams.

Design, build, and operate scalable, secure LLM/NLP and ML systems; develop production code; drive architecture and MLops; monitor models in production; advise leadership and improve data/feedback loops to reduce effort and improve operational outcomes.

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.

Lead AI and innovation for travel products by identifying AI/ML opportunities, deploying generative AI and LLMs, managing Agile product delivery, defining epics/user stories, coordinating cross-functional teams, and measuring product metrics to improve efficiency, reduce costs, and drive revenue.

Lead and deliver secure, high-quality full‑stack Java applications; provide technical guidance, drive AI-assisted engineering practices and governance, implement REST APIs, mentor teams, and promote reusable automation across the SDLC.