Teams at Crayon Data

Develop and deploy machine learning and AI solutions for risk assessment, fraud detection, customer intelligence, personalization, and predictive modeling, particularly in financial services. Translate experiments into scalable production systems, identify data-driven business opportunities, collaborate with engineering, product, and business stakeholders, prototype emerging techniques, and communicate solutions to clients. The role requires strong analytical, technical, communication, and stakeholder management skills.

Deploys, integrates, optimizes, and operationalizes enterprise generative AI platforms in secure, on-premise, and air-gapped banking environments. Responsibilities include managing OpenShift and Kubernetes workloads, NVIDIA GPUs, LLM inference, containerized deployments, Helm and Kustomize configurations, GitOps and CI/CD pipelines, and secure infrastructure integrations. The engineer troubleshoots deployment, networking, and performance issues while collaborating directly with client IT, security, infrastructure, engineering, and AI teams.

Own the end-to-end architecture of secure, enterprise-grade Generative AI and AI infrastructure platforms for regulated banking environments. Design GPU-accelerated, on-premise, air-gapped platforms using NVIDIA technologies, OpenShift, Kubernetes, RAG, LLMs, graph databases, and enterprise data pipelines. Provide technical leadership, establish security and data-sovereignty standards, guide AI and engineering teams, and evaluate emerging technologies for measurable business impact.