Direct message the job poster from Virtue AI Virtue AI is at the forefront of AI security. As enterprises increasingly adopt Large Language Models, the need for robust, trustworthy, and safe AI has never been greater. Our mission is to build the essential guardrails and red-teaming tools that enable organizations to deploy multi-modal AI applications confidently and responsibly. We are a well-funded, early-stage startup founded by industry veterans, and we're looking for passionate builders to join our core team. Are you a high-performing, motivated engineer ready to make a significant impact in the AI security space? Virtue AI is seeking talented AI Platform Engineer to join us. We are a fast-paced, customer-focused company with cutting-edge technology, strong early customer traction, and market dominance. If you thrive in an environment that values hard work, collaboration, and technical curiosity, we want to hear from you. About the Role As a foundational ML Platform Engineer at Virtue AI, you’ll design and build the platform that powers all stages of the VirtueAI’s ML lifecycle from experimentation and fine-tuning to deployment across both SaaS and on-prem customer managed infrastructure. You'll work very closely with ML engineers and scientists to create scalable, secure, and abstracted workflows that enable rapid iteration and robust product delivery. This is a very high-impact role focused on building tools, SDKs, and services that make ML development faster, more reproducible, and deployment-ready - whether we’re running on Vertex AI, GKE, Sagemaker or a customer's private Kubernetes cluster. While building more accurate models is not a direct responsibility, there will be ample opportunity to work with AI pipelines in this role. Responsibilities Develop SDKs, CLIs, APIs that expose platform capabilities (training & fine-tuning pipelines, inference stacks, feedback loops, quantization, and deployment) in a consistent, developer-friendly way across heterogeneous environments (e.g., Vertex AI, GKE, Together.ai). Design and build core components of our ML platform, including model registries, experiment tracking integrations (W&B), and cataloging systems for red-teaming, guardrails and agentic datasets and models. Create tooling that supports reproducibility, version control, and benchmarking of models and datasets across internal R&D, on-prem and SaaS. Build robust abstractions over infrastructure (e.g., Vertex AI, Ray, Kubernetes), enabling ML practitioners to run jobs and deploy models without needing to understand low-level infra. Develop and maintain internal libraries that integrate with CI/CD, model tracking, QA, and security/compliance review workflows. Implement lightweight and performant observability tooling across training and inference platforms (e.g., GPU utilization and performance (accuracy, latency, throughput) regressions). Help define the model packaging and deployment interfaces used across our on-prem (Helm, Docker, HuggingFace, Github Actions) and cloud environments. Collaborate with security and compliance stakeholders to ensure platform tools are secure, tenant-aware, and audit-ready. Support internal onboarding and usability by contributing to platform documentation, quickstarts, and dev support workflows. Qualifications Strong backend and cloud engineering skills and experience building developer-facing libraries, APIs, or tools. Strong product sense for internal platforms: you think in interfaces, reusability, and team velocity. Experience building ML infrastructure or tooling for training, tuning, and inference workflows. Comfortable working with systems like Sagemaker, Vertex AI, Kubernetes, Ray, or similar distributed compute environments. Strong familiarity with the ML development lifecycle and tools like W&B, HuggingFace, MLFlow, DVC, or custom registries. Proficiency in Python (strongly preferred) and cloud infrastructure. Experience with containerized deployment patterns (e.g., Docker, Helm charts) and infrastructure abstraction. Bonus: Familiarity with hybrid deployment models (SaaS + on-prem), SOC 2/ISO 27001 workflows, or vLLM/NVidia Triton integration. Required Skills Owning the technical glue between ML, infra, and product — and delivering leverage to the entire engineering team. Developing shared libraries and APIs that unify job launch, monitoring, and evaluation across Vertex AI, Kubernetes, and custom GPU providers. Creating self-service tooling and interfaces that allow ML and forward-deployed engineers to work independently while ensuring reliability. Implementing robust model registries and packaging standards that work across CI/CD pipelines and deployment environments. Designing systems to curate, version, and catalog red-teaming and guardrails datasets — enabling consistent reuse, traceability, and benchmarking across experiments. Supporting on-prem use cases by standardizing packaging, configuration, and deployment flows for customer K8s clusters (e.g., Helm charts, abstracted CLI tools). Virtue AI, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), sexual orientation, gender identity or expression, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic or background protected by federal, state, or local law. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function Industries Software Development Referrals increase your chances of interviewing at Virtue AI by 2x Sign in to set job alerts for “Platform Engineer” roles. 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