• Design, develop, and deploy scalable AI/ML solutions for real-world healthcare and operational use cases. • Own end-to-end ML pipelines: data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. • Develop and optimize automation systems (e.g., intelligent customer email order processing). • Build and improve conversational AI and voice agents for sales and support teams. • Contribute to computer vision initiatives, including automation projects in logistics. • Translate complex business requirements into well-structured technical solutions. • Ensure performance, scalability, monitoring, and reliability of models in production. • Collaborate cross-functionally with product, business, and engineering stakeholders. • Contribute to technical discussions, innovation sessions, and continuous improvement initiatives.

• Only candidates based in APAC will be considered for this position. • Design and deploy healthcare-focused Agentic AI systems capable of autonomous, multi-step task execution (e.g., appointment scheduling, eligibility verification, intake automation, triage routing). • Architect and optimize LLM-powered conversational agents across voice and digital channels. • Develop Retrieval-Augmented Generation (RAG) architectures to power contextual, domain-specific healthcare knowledge systems. • Engineer robust prompt frameworks, safety guardrails, and evaluation pipelines tailored to regulated healthcare environments. • Continuously evaluate and improve agent performance, accuracy, and safety through structured experimentation and analytics. • Architect advanced IVR modernization strategies and intelligent voice workflows. • Optimize ASR (speech-to-text) and TTS (text-to-speech) systems to meet healthcare-grade accuracy and reliability standards. • Integrate conversational AI into enterprise contact center platforms and CPaaS environments. • Ensure seamless interoperability with EHR/EMR systems using healthcare standards such as FHIR and HL7. • Design scalable, fault-tolerant architectures supporting high-availability healthcare operations. • Develop NLP pipelines for clinical document summarization, coding support, PHI detection, and structured data extraction. • Architect HIPAA-compliant AI systems with secure data handling, encryption, and role-based access controls. • Implement monitoring, observability, and analytics frameworks to measure operational efficiency and patient experience outcomes. • Maintain alignment with evolving healthcare AI regulatory and compliance requirements. • Contribute reusable AI templates that power no-code and low-code deployment models. • Build and maintain APIs and SDK integrations that allow enterprise customers to embed AI capabilities rapidly. • Collaborate cross-functionally with product, solution engineering, and co-creation teams to accelerate customer time-to-value. • Mentor junior engineers and define best practices for deploying Agentic AI in regulated industries. • Provide internal technical leadership on scalable AI architecture and deployment standards.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description At Mercor, we believe the safest AI is the one that’s already been attacked — by us. That’s why we’re building a pod of AI Red-Teamers: human data experts who probe AI models with adversarial inputs, surface vulnerabilities, and generate the red-team data that makes AI safer for our customers. This role may include reviewing AI outputs that touch on sensitive topics such as bias, misinformation, or harmful behaviors. All work is text-based, and participation in higher-sensitivity projects is optional and supported by clear guidelines and wellness resources. What You’ll Do - Red-team AI models and agents: jailbreaks, prompt injections, misuse cases, exploits - Generate high-quality human data: annotate failures, classify vulnerabilities, and flag systemic risks - Apply structure: follow taxonomies, benchmarks, and playbooks to keep testing consistent - Document reproducibly: produce reports, datasets, and attack cases customers can act on - Flex across projects: support different customers, from LLM jailbreaks to socio-technical abuse testing Qualifications - You bring prior red-teaming experience (AI adversarial work, cybersecurity, socio-technical probing) - You’re curious and adversarial: you instinctively push systems to breaking points - You’re structured: you use frameworks or benchmarks, not just random hacks - You’re communicative: you explain risks clearly to technical and non-technical stakeholders - You’re adaptable: thrive on moving across projects and customers Requirements - Adversarial ML: jailbreak datasets, prompt injection, RLHF/DPO attacks, model extraction - Cybersecurity: penetration testing, exploit development, reverse engineering - Socio-technical risk: harassment/disinfo probing, abuse analysis - Creative probing: psychology, acting, writing for unconventional adversarial thinking Benefits - Build experience in human data-driven AI red-teaming at the frontier of safety - Play a direct role in making AI systems more robust, safe, and trustworthy - The pay rate for this role may vary by project, customer, and content category. Compensation will be aligned with the level of expertise required, the sensitivity of the material, and the scope of work for each engagement.

• Design, build, and operate GEN-AI powered, data-intensive systems that process and learn from large-scale event streams • Ship reliable LLM features, prove they work via rigorous evaluation, and continuously improve quality through context engineering • Build and maintain production-grade GEN-AI services in Python • Write clean, maintainable, and efficient code • Optimize applications for performance and scalability