AI Platform Engineer

About Us
Modus Create is a digital consulting partner that helps ambitious organizations design, build, and modernize digital products, platforms, and AI-powered experiences. We work shoulder-to-shoulder with client teams to move from idea to production quickly and sustainably, with a clear path to scale. Consultant | Remote

About Us
Modus Create is a digital consulting partner that helps ambitious organizations design, build, and modernize digital products, platforms, and AI-powered experiences. We work shoulder-to-shoulder with client teams to move from idea to production quickly and sustainably, with a clear path to scale. Founded in 2011, Modus is a global, fully remote team of world-class technologists who thrive in a collaborative, innovative environment. We're a digital product engineering partner for forward-thinking businesses. Our global teams work side-by-side with clients to design, build, and scale custom solutions that achieve real results and lasting change, partnering with industry leaders including AWS, GitHub, and Atlassian. We were fully remote before it was cool Recognized as one of the Inc. 5000 Fastest Growing Private Companies for nine years and a top remote work company by FlexJobs, we have helped some of the world's largest brands deliver powerful digital experiences. Opportunity We are seeking an experienced Platform Engineer to design, build, and operate secure, scalable cloud platforms that enable client teams to ship faster and with confidence. You'll work embedded within cross-functional product teams—partnering with developers, DevOps engineers, security teams, and product leaders—to reduce deployment friction and improve platform reliability. As a Platform Engineer (Consultant/Sr. Consultant), you take ownership of your platform deliverables, advise on architecture decisions, and help unblock teams. You design and implement cloud infrastructure, build deployment pipelines, operationalize observability, and contribute to platform security and compliance. Take on greater technical responsibility as you deepen your expertise. You'll work hands‑on with AI infrastructure—deploying LLM applications, managing vector databases for RAG systems, securing agentic workflows, and integrating AI services into platform architecture. You'll also support teams adopting AI‑assisted development tools (GitHub Copilot, Claude, Cursor) and ensure they're integrated safely into platform workflows. You'll own the operational and security posture of these systems, monitoring model performance, preventing prompt injection attacks, ensuring data governance, and helping teams scale AI reliably to production. The Platform Engineer will work shoulder-to-shoulder with cross‑capability teams to ensure resilient, scalable platform adoption across client environments. You'll also have opportunities to advise leadership on platform strategy and direction—helping shape how organizations modernize their infrastructure and scale their engineering practices. Requirements 3-8 years hands‑on platform, infrastructure, DevOps, or cloud engineering Familiarity with LLM APIs, vector databases, or AI model serving Familiarity with AI‑assisted development tools (GitHub Copilot, Claude, Cursor) Understanding of AI security: prompt injection, data governance, model monitoring, output validation Strong proficiency supporting and building in one cloud platform (AWS, Azure, GCP) Deep experience with infrastructure-as-code (Terraform, CloudFormation, Pulumi, etc.) Solid understanding of Kubernetes, Docker, and CI/CD pipeline design Hands‑on deployment automation, observability, and incident response Ability to troubleshoot distributed systems and production issues; comfort with on‑call Clear communication; able to explain technical tradeoffs to technical and non‑technical audiences Scripting proficiency (Python, Bash, Go); Linux/Unix administration fundamentals Version control (Git), collaboration workflows, and infrastructure automation

Key Responsibilities
& Deliverables Deploy, operate, and secure AI infrastructure (LLM serving, vector stores, agentic systems) Work with AI‑assisted development tools and help teams integrate them safely into platforms Advise on AI deployment patterns and operational best practices in integration reviews Design, implement, and operate cloud architectures across dev, staging, and production Build and maintain deployment pipelines, CI/CD automation, and infrastructure‑as‑code Implement observability systems (metrics, logging, tracing) for incident response Manage cloud infrastructure provisioning, configuration, and security while optimizing for reliability, cost, and compliance Monitor and optimize cloud resources for cost and performance Collaborate with development, DevOps, and security teams on controls and best practices Troubleshoot platform issues, participate in incident response, and drive improvements Create runbooks, architecture documentation, and troubleshooting guide