RYZ Labs - Principal AI Engineer
Requirements
• Experience: 15+ years of experience in software engineering, data science, or a closely related technical field. • Education: Bachelor’s degree or higher in Computer Science, Engineering, or a related field. • AI/ML & Cloud: Deep expertise in the Python ecosystem and AI/ML frameworks, with proven experience deploying models to public cloud infrastructure. • Architecture: Demonstrated track record designing and operating multi-tenant AI services, LLM integrations, and microservices architectures in production. • DevOps & Infrastructure: Strong hands-on command of containerization (Docker), Infrastructure-as-Code (Terraform), and modern CI/CD practices. • AI Security & Governance: Substantive experience with AI/LLM security risks (prompt injection, data boundary enforcement, model supply chain risks, threat modeling). • Communication: Exceptional written and verbal English skills, with the ability to articulate complex technical ideas to diverse audiences and senior stakeholders. • Mentorship & Culture: Passion for coaching, developing engineers, and advocating for engineering best practices. • Prior experience applying AI/ML within business consulting, advisory, or professional services environments. • Hands-on experience with emerging AI integration protocols and gateway design patterns. • Active contributions to open-source AI/ML projects, publications, or technical communities. • Proficiency in additional programming languages (e.g., Go, TypeScript). • Advanced certifications in Cloud, AI/ML, or Security (AWS, GCP, Azure ML, CISSP, etc.). • Experience with compliance frameworks such as SOC2, ISO 27001, or TISAX.
Responsibilities
• AI Productization & Platform Engineering • Design and own the playbook for AI productization and governance, defining service patterns, security standards, evaluation rubrics, and production-readiness criteria. • Build reusable internal tooling, including AI scaffolding, monitoring systems, and data infrastructure designed for widespread adoption. • Establish robust AI agent governance policies (permissions, code execution controls, system access, human-in-the-loop enforcement). • Partner with Security, Legal, and Compliance teams to define SOC2/ISO-aligned AI controls, vendor requirements, and data classification policies. • Define standardized deployment patterns using containerization, Infrastructure-as-Code (IaC), and reusable CI/CD pipelines. • Champion AI-assisted development practices, test-driven development (TDD), and modern software engineering standards across the organization. • Codify engineering baselines (CI/CD, DevOps, testing, delivery quality) for new and re-platformed products. • Mentor engineers and tech leads, driving measurable improvements in design quality, consistency, and production rigor. • Cross-Functional Leadership & Strategy • Act as the primary technical authority on AI/ML, platform architecture, and modern engineering practices. • Bridge the gap between technical and non-technical stakeholders, translating complex architectural decisions, AI risk topics, and platform trade-offs into actionable executive guidance.
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