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AI Reliability & Red Teaming Engineer

technopalsRemote, IndiaRemotefull timeSenior

Expired

Job description

Role OverviewYour 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 ResponsibilitiesRed 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.QualificationsTechnical 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.Role OverviewYour 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 ResponsibilitiesRed 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.QualificationsTechnical 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.Role OverviewYour 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 ResponsibilitiesRed 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.QualificationsTechnical 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.Technopals Pte.Ltd. is an Equal Opportunity Employer and does not discriminate on the basis of race or ethnicity, religion, sex, national origin, age, veteran disability or genetic information or any other reason prohibited by law in employment.I'm interested{{criteriaIndex}} {{field}} {{captialize(condition)}} ( ) {{cxPropHelpText}} {{cxPropField.consent_details.description}} {{record.Posting_Title}} {{record.Posting_Title}} {{record.Posting_Title}} {{candidate.Email}} {{topMessage}} Job Details {{topMessage}} previous next {{title}} {{score}}% Job Description {{unescape(sanitizeHTML(descriptionHTML))}} {{message}} Step {{curStepInMandatorySecPrompt}}/{{totalNumOfStepsInMandatorySecPrompt}} For privacy and security purposes, please go through the following points and provide consent. Accept Decline {{getI18n("zr.careers.publicpage.meta.joblisting")}} {{getI18n("zr.careers.publicpage.meta.joblisting")}} {{cxPropAssessment.name}} {{getI18n("zr.questionnaire.question.information.heading")}} {{getI18n("zr.cw.autofill.head")}} {{cxPropBodyMessage}} {{getI18n("zr.careers.autopopulate.success")}} {{getI18n("zr.cw.apply.sign",cxPropCompanyInfo.name)}} {{if(cxPropDetails.applyWithSeekButton,cxPropDetails.applyWithSeekButton.buttonLabel,getI18n("zr.quickapply.apply.toggle","Seek"))}} {{getI18n('zr.eeo.questionnaire.portal.maintitle')}} {{botName}} {{getI18n("zr.zia.sb.consent.msg1")}}{{criteriaIndex}}{{criteriaIndex}}{{field}} {{captialize(condition)}}{{field}}{{captialize(condition)}}( )(){{cxPropHelpText}}{{cxPropHelpText}}{{cxPropField.consent_details.description}}

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