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:

São José dos Campos, São Paulo, Brazil

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, design, and code reviews to ensure quality, security, scalability, maintainability, and regulatory readiness. 

• Troubleshoot complex distributed systems spanning data platforms, AI services, applications, infrastructure, and cloud services. 

• Mentor engineers and establish data, AI, and software engineering standards while remaining hands-on in critical delivery activities. 

Required Qualifications 

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field. 

• 6+ years of engineering experience, including substantial experience designing and delivering enterprise-scale data, AI, analytical, or cloud platforms. 

• Advanced proficiency in Python, PySpark, SQL, and distributed data-processing patterns. 

• Hands-on experience with Databricks, Azure Data Factory, data lake or lakehouse architectures, and Azure cloud data services. 

• Experience designing batch and streaming data pipelines, reusable data products, analytical data models, and data integration solutions. 

• Strong understanding of data quality, metadata, lineage, data contracts, schema evolution, performance optimization, observability, security, and governance. 

• Experience developing or integrating applied AI and Generative AI solutions, including LLM-enabled applications. 

• Experience operationalizing AI or ML workloads through automated deployment, monitoring, lifecycle management, and governance. 

• Strong software engineering fundamentals, including modular design, automated testing, version control, APIs, algorithms, data structures, distributed systems, and architecture. 

• Experience designing cloud-native services and deploying containerized workloads using Docker and Kubernetes, preferably Azure Kubernetes Service. 

• Experience with Azure cloud-native architecture, CI/CD, infrastructure automation, authentication, authorization, and enterprise security controls. 

• Practical experience developing APIs, backend services, and fit-for-purpose user interfaces using modern frameworks. 

• Strong communication, technical leadership, stakeholder management, customer/vendor mangement, collaboration, mentoring, and problem-solving skills. 

• Work with offshore teams and ensure the best practices are adopted during the delivery 

• Experience with IT finance managing SOWs and resource planning 

• Ability to travel up to 5%, including international travel. 

Preferred Qualifications 

• Advanced Databricks and lakehouse architecture experience. 

• Experience with streaming technologies and event-driven data processing. 

• Knowledge of Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI, prompt engineering, AI evaluation, and LLM application development. 

• Experience with MLflow or comparable tools for experiment tracking and model lifecycle management. 

• Experience with ZenML or comparable ML platform orchestration frameworks. 

• Experience implementing data and AI governance in regulated environments. 

• Experience with GitOps deployment practices using ArgoCD or similar. 

• Experience with observability platforms such as OpenTelemetry, Grafana, Prometheus, and Azure Monitor. 

• Experience with TypeScript, React, Node.js, NestJS, Vite, TypeORM, Nx Monorepo, NGINX, ingress controllers, or comparable technologies. 

• Familiarity with ERP/SAP platforms and enterprise integration patterns. 

• Knowledge of IoT, edge computing, and industrial data solutions. 

What Makes This Role Exciting 

• Shape enterprise-scale Data and AI platforms that support healthcare innovation. 

 

 

Required Skills:

 

 

Preferred Skills:

Advanced Analytics, Agility Jumps, Analytical Reasoning, Coaching, Critical Thinking, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Quality, Data Science, Hybrid Clouds, Motivating People, Problem Solving, Process Improvements, Technologically Savvy