SW QMS Specialist

Katalyst Talent Agency
South Plainfield, NJ, United States
14 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$40,000.0 - $55,000.0
Working hours
Regular working hours

Tech stack

Document Management Systems Software Systems

Job description

Benefits/Perks Highly enriched & healthy work environment Excellent growth opportunities Competitive Pay Career Advancement Job Summary We are seeking an experienced Qual…

  • 9 days ago

Requirements

  • Bachelor’s degree in Quality Management, Regulatory Affairs, Life Sciences, or a related field.
  • Minimum of 5 years of experience in QMS, quality assurance, regulatory compliance, or related roles.
  • Software/AI experience.
  • Strong knowledge of FDA and EU regulatory requirements, specifically with QMS.
  • Experience with CAPA processes, document management, and regulatory submissions.
  • Familiarity with cybersecurity audit procedures and standards.
  • Excellent communication skills, with a keen attention to detail and ability to manage multiple priorities.

Preferred Qualifications:

  • Certification in Quality Management or Regulatory Affairs (e.g., ASQ, RAPS).
  • Hands-on experience with QMS software systems.
  • Experience in the medical device or healthcare tech industry.

Benefits & conditions

  • $94,000-151,800 per year At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,whe…

  • 6 days ago +

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

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Background and career journey in regulated software systems

Martin Hynie · Coffee With Developers

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Transforming the software engineering role instead of replacing it

Daniel Tao +3 · World Congress 2024

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Experiencing Conway's Law in heterogeneous software systems

Michael Jaeger Michael Jaeger · Europe 2026 Virtual

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Reducing systemic complexity with universal converged databases

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Documenting failures to navigate AI hype and bias

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