Senior Data & Applied AI Engineer

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.  We provide an inclusive work environment where each person is considered as an individual.  At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Engineering

Job Category:

Scientific/Technology

All Job Posting Locations:

Raritan, New Jersey, United States of America

Job Description:

Johnson & Johnson is currently recruiting for a  Senior Data & Applied AI Engineer located in North America or LATAM. 

At Johnson & Johnson, we believe health is everything. For more than 130 years, we have worked to make a meaningful difference in the lives of people around the world through science, innovation, and technology. We foster an inclusive environment where diverse experiences, perspectives, and ideas drive breakthrough solutions that improve human health. 

We are seeking a highly skilled and hands-on Senior Data & Applied AI Engineer with deep expertise in Data and AI Engineering to design, build, and scale enterprise data products, AI solutions, and MLOps platforms. The role will lead the development of reliable data pipelines, reusable data products, AI-enabled services, and model operationalization capabilities. The engineer will also apply cloud-native, API, and full-stack development skills to integrate these capabilities into secure, scalable enterprise solutions. 

The ideal candidate is a strong technical leader with deep experience in modern data engineering and applied AI, complemented by practical expertise in MLOps, cloud-native architectures, APIs, and application development. This individual will establish engineering standards, mentor team members, and help translate emerging technologies, including Generative AI and Agentic AI, into governed, production-ready enterprise capabilities. 

Key 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, des…