Senior Data & Applied AI Engineer

About the role

Johnson & Johnson is recruiting for a Senior Data & Applied AI Engineer located in North America or LATAM. The role focuses on designing, building, and scaling enterprise data products, AI solutions, and MLOps platforms, leading development of reliable data pipelines, reusable data products, AI-enabled services, and model operationalization capabilities, applying cloud-native, API, and full‑stack development skills to integrate these into secure, scalable enterprise solutions.

Responsibilities

• Design, build, and optimize scalable batch and streaming data pipelines using Databricks, PySpark, SQL, Azure Data Factory, and Azure cloud data services.
• Develop reusable, governed data products and analytical datasets that support enterprise reporting, advanced analytics, and AI use cases.
• Design and implement AI and Generative AI solutions, including retrieval, orchestration, evaluation, integration, and production operationalization.
• Build and operate MLOps capabilities that support model development, deployment, monitoring, lineage, governance, and lifecycle management.
• Define data architecture, data quality, metadata, observability, security, and performance standards for enterprise data and AI platforms.
• Develop cloud‑native platform services, microservices, REST APIs, event‑driven components, and enterprise integrations that expose data and AI capabilities.
• Build fit‑for‑purpose user experiences and full‑stack applications using TypeScript, React, Node.js, and comparable technologies where required to operationalize data and AI solutions.
• Create reusable platform components, shared libraries, templates, CLI tools, and developer productivity solutions.
• Build and maintain CI/CD pipelines, DevOps automation, GitOps workflows, and infrastructure‑as‑code solutions.
• Implement secure authentication, authorization, RBAC, auditability, secrets management, and enterprise security controls.
• Implement observability across data pipelines, AI services, applications, and infrastructure, including logging, monitoring, tracing, data‑quality monitoring, and performance management.
• Collaborate with data scientists, product teams, architects, and business stakeholders to translate business needs into scalable data and AI solutions.
• Lead proofs of concept, technical evaluations, and innovation initiatives focused on data, AI, and platform technologies.
• Conduct architecture, design, and code reviews to ensure quality, security