Senior Data & Applied AI Engineer

Johnson & Johnson

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Raritan, NJ
Salary
$94,000–$151,800 / yr
Posted
3 days ago
Freshness
Confirmed live today
Closes
Oct 3, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $187k
This role $123k
$78k most similar roles pay here $241k

This role pays less than 97% of similar roles. Most pay $151,000–$223,750 — the shaded band above. At the midpoint, this role pays about $123k versus about $187k for comparable roles.

Based on 240 similar postings.

Employer

About Johnson & Johnson

Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices

Johnson & Johnson currently has 63 open roles on FindRole.

Listed pay typically runs $117,000–$201,250 across 54 roles with salary data.

Most-posted roles

View all roles at Johnson & Johnson

At a glance

TL;DR · Senior Data & Applied AI Engineer

The Senior Data & Applied AI Engineer joins a team focused on building enterprise-scale data and AI platforms to support healthcare innovation. This role involves designing, building, and scaling reliable data pipelines, reusable data products, and MLOps platforms for model operationalization. The engineer will develop batch and streaming pipelines using Databricks, PySpark, SQL, Azure Data Factory, and Azure cloud services. They will also implement Generative AI solutions involving retrieval, orchestration, and evaluation while building full-stack applications using TypeScript, React, and Node.js. Key technical requirements include proficiency in Python, Docker, Kubernetes, and CI/CD workflows. The role addresses the challenge of translating emerging technologies like Agentic AI into governed, production-ready capabilities within a regulated environment. The engineer will establish engineering standards, mentor team members, and manage complex distributed systems to deliver scalable data solutions.

What you'll do

  • Design, build, and optimize scalable batch and streaming data pipelines using Databricks, PySpark, and Azure cloud services.
  • Develop reusable, governed data products and analytical datasets to support enterprise reporting and AI use cases.
  • Implement Generative AI solutions including retrieval, orchestration, evaluation, and production operationalization.
  • Build and operate MLOps capabilities for model development, deployment, monitoring, lineage, and lifecycle management.
  • Develop cloud-native microservices, REST APIs, and full-stack applications to expose data and AI capabilities.
  • Establish engineering standards, conduct code reviews, and mentor team members on data and software engineering best practices.
  • Maintain CI/CD pipelines, DevOps automation, and infrastructure-as-code solutions for enterprise platforms.
  • Implement observability across data pipelines and infrastructure including logging, monitoring, and performance management.

What we're looking for

  • Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.
  • 6+ years of engineering 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/lakehouse architectures, and Azure cloud services.
  • Experience designing batch and streaming data pipelines, reusable data products, and analytical data models.
  • 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, and distributed systems.
  • Experience with cloud-native services, containerized workloads (Docker/Kubernetes), CI/CD, and infrastructure automation.

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