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AI Reliability & Red Teaming Engineer

Role overview

Qualifications

  • Expert Python skills
  • Deep experience with PyTorch, TensorFlow, and MLOps pipelines
  • BS/MS in Data Science, Mathematics, or Cybersecurity
  • 4+ years in ML Engineering with a focus on model security or reliability

Responsibilities

  • Design and execute adversarial attacks (prompt injection, data poisoning) against LLMs and predictive models
  • Build and maintain the 'Continuous Control' stack using tools like Evidently AI or WhyLabs
  • Partner with Data Scientists to implement algorithmic fairness techniques when bias is detected

About the company

Technopals Pte Ltd logo

Technopals Pte Ltd

Technopals is one of the leading providers of IT solutions and services to various companies of high repute. Since its inception in 2009, Technopals has grown in size and stature, by efficiently implementing IT related solutions and services to Global organizations in Asia Pacific. Technopals in recent has ventured into IT Security Services catering Global Customers. As part of business growth Technopals is now equipped with proficiency in IT Talent Acquisition along with providing complete IT Security Services, IT Infrastructure Enterprise Solutions and Software Development. Technopals follows the leading industry practices to deliver the top most service level to our clients. Backed by rich expertise in all core technologies to cater to the IT infrastructure needs of an organization, our approach is to assist small, medium and large businesses to achieve their true potential by reaping all the benefits of IT. www.technopals.com EA Licence # 09C3442

Company details

Company size201 - 500

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Job description

Role Overview
​Your mission is to break our models before someone else does. You will lead the technical stress-testing of our AI systems, focusing on Adversarial Robustness, Bias detection, and Hallucination mitigation.
​Key Responsibilities
​Red Teaming: Design and execute adversarial attacks (prompt injection, data poisoning) against LLMs and predictive models.
​Automated Monitoring: Build and maintain the "Continuous Control" stack using tools like Evidently AI or WhyLabs.
​Bias Remediation: Partner with Data Scientists to implement algorithmic fairness techniques when bias is detected.
​Qualifications
​Technical Stack: Expert Python skills; deep experience with PyTorch, TensorFlow, and MLOps pipelines.
​Education: BS/MS in Data Science, Mathematics, or Cybersecurity.
​Experience: 4+ years in ML Engineering with a focus on model security or reliability.

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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