Senior Data & Applied AI Engineer

Johnson & Johnson•Raritan, NJ

9d•$94,000 - $151,800•Hybrid

About The Position

Johnson & Johnson is 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.

Requirements

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.

Nice To Haves

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.

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.
Work with offshore teams and ensure the best practices are adopted during the delivery.
Experience with IT finance managing SOWs and resource planning.

Benefits

Employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period
10 days Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year