Top AI & Machine Learning Jobs

Lead design and delivery of enterprise-scale generative AI and agentic systems. Define architecture, integrate foundation models, build cloud-native infrastructure and MLOps pipelines, implement orchestration and monitoring, and mentor teams to productionize secure, scalable AI solutions aligned with business goals.

Lead AI and generative-AI transformation programs for banking clients: define AI strategy and roadmaps, advise executives across banking lines, partner with architecture and delivery teams to pilot and scale solutions, mentor consulting teams, and drive business development and thought leadership.

Lead AI product strategy and roadmaps for financial services clients; identify high-value use cases, run prototyping/POCs, and guide integration of LLMs, RAG, embeddings, and agentic AI into enterprise platforms while advising senior stakeholders and cross-functional teams.

Lead design and delivery of enterprise-scale generative AI and agentic systems. Define architecture, integrate foundation models, establish cloud-native infrastructure and MLOps pipelines, orchestrate workflows, mentor teams, and drive production-grade AI deployments aligned to business goals.

Lead design and delivery of enterprise-scale generative AI and agentic systems: define architecture, integrate foundation models, build cloud-native infrastructure and MLOps pipelines, orchestrate workflows and integrations, mentor teams, and guide solutions from concept to production.

Design, build, and deploy GenAI applications on GCP using LLMs. Implement RAG architectures, integrate LLM APIs (OpenAI/Vertex AI), apply prompt engineering, build REST APIs/microservices, and collaborate with cross-functional teams to deliver production-ready, scalable AI solutions in agile, client-facing environments.

Design, train, fine-tune and deploy LLM and neural-network based solutions for banking use cases. Build multi-agent workflows, data/model pipelines, production-grade backend services, and ensure regulatory, privacy, and ethical compliance. Collaborate with stakeholders, monitor production systems, participate in CI/CD and agile processes.

Design and deliver semantic-data solutions and knowledge graphs to make enterprise information usable for AI and analytics. Build semantic models, data pipelines, integrations with LLMs and retrieval systems, implement validation and CI/CD, support production deployments, mentor junior staff, and contribute to architecture, evaluations, and reusable accelerators.

Lead design and delivery of enterprise semantic-data solutions and knowledge graphs, integrating structured and unstructured data for AI/ML and analytics. Build semantic pipelines, graph-backed services, and integrations (Python/Java), support NLP/embedding use cases, evaluate platforms, define governance/quality controls, run workshops, mentor teams, and drive prototypes through production for regulated clients.

Lead development and deployment of production ML models and analytics for wealth management and retail banking. Analyze large datasets, design predictive solutions (segmentation, churn, personalization, risk), build data pipelines and feature engineering, monitor model performance, drive MLOps and responsible AI practices, and communicate insights to stakeholders.