AI Engineer - AI

AI Engineer - Responsible AI page is loaded## AI Engineer - Responsible AI locations:
Remote Work( USA) time type:
Full time posted on:
Posted Todayjob requisition :
JR106871
** About Centific
** Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets;

fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation(TM) solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.

Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.
** About Job
**** Role: AI Engineer - Responsible AI
***
* Location:

Seattle, WA | Palo Alto, CA | Remote
**** Type:
Full-time
**** Build the Future of Safe and Responsible AI
**** The Mission
** We're tackling cutting-edge AI safety across adversarial robustness, jailbreak defense, agentic workflows, and human-in-the-loop risk modeling. As an AI Engineer, you'll own high-impact projects from research conception through production deployment, directly contributing to our platform's security guarantees while building scalable, maintainable infrastructure.
** What You'll Do*
* • Advance AI Safety:
Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (prompt injection, obfuscation, narrative red teaming) and deploy them as production-grade services.
• Build Scalable Safety

Infrastructure: Architect and deploy distributed safety evaluation pipelines handling millions of requests, with real-time monitoring, alerting, and incident response capabilities.
• Large-Scale Data Engineering:
Design ETL pipelines for processing terabytes of safety-related data (attack patterns, behavioral logs, model outputs); build data lakes and feature stores for safety ML systems.
• Evaluate AI Behavior:
Analyze and simulate human-AI interaction patterns at scale to uncover behavioral vulnerabilities, social engineering risks, and over-defensive vs. permissive response tradeoffs.
• Agentic AI Security:
Build production workflows for multi-agent safety (agent self-checks, regulatory compliance, defense chains) spanning perception, reasoning, and action.
• MLOps & Model Deployment:
Deploy safety models to production using containerized microservices, implement CI/CD pipelines for model updates, and manage model versioning and A/B testing infrastructure.
• Benchmark & Harden LLMs:
Create reproducible, automated evaluation protocols for safety, over-defensiveness, and adversarial resilience across diverse models with continuous integration.
** Example Problems You Might Tackle*
* • Production Red-Teaming Platform:
Build and operate an automated red-teaming infrastructure that continuously probes advanced LLMs (GPT-4o, GPT-5, LLaMA, Mistral, Gemma) at scale, with dashboards and alerting.
• Real-Time Defense Systems:
Implement context-aware, multi-turn attack detection and guardrail mechanisms as low-latency services handling 10K+ requests per second.
• Agent Self-Regulation at Scale:
Develop agentic architectures for autonomous self-check and self-correct with distributed orchestration and fault tolerance.
• Safety Data Platform:
Design and build data infrastructure for collecting, storing, and analyzing petabyte-scale safety telemetry with streaming analytics.
** Minimum Qualifications*
* • Master's degree in CS/EE/ML/Security or related field (Ph.D. preferred)
• 2+ years of industry…