[Hiring] AI Platform Engineer @Modus Create

Role Description

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 will:

• Take ownership of your platform deliverables.
• Advise on architecture decisions and help unblock teams.
• Design and implement cloud infrastructure.
• Build deployment pipelines and operationalize observability.
• Contribute to platform security and compliance.
• 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.
• Support teams adopting AI-assisted development tools (GitHub Copilot, Claude, Cursor) and ensure they're integrated safely into platform workflows.
• 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.
• Work shoulder-to-shoulder with cross-capability teams to ensure resilient, scalable platform adoption across client environments.
• Advise leadership on platform strategy and direction—helping shape how organizations modernize their infrastructure and scale their engineering practices.

Qualifications

• 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.

Requirements

• 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 guides.
• Review infrastructure changes and contribute to architecture standards.
• Tune systems for latency, throughput, and resource efficiency.

Team Collaboration

• Availability for regular working sessions and discovery workshops with client teams.
• Overlap with client business hours daily is expected.
• Reliable high-speed internet is a must.
• Ability to work independently while collaborating with client & Modus leaders.
• Documenting solutions, sharing learnings with the team, contributing to internal runbooks.
• Pair program and collaborate on complex technical problems.
• Welcome code and architecture reviews; seek input on designs.
• Ability to coordinate with security, product, and other teams beyond just infrastructure/dev.

Bonus Skills

• Experience designing or operating internal developer platforms.
• Experience with AI-assisted development tools (GitHub Copilot, Claude, Cursor) and their platform/security considerations.
• Familiarity with SRE practices, reliability engineering, and incident management.
• Cloud security and compliance experience (IAM, secrets management, audit readiness).
• Mentoring junior engineers or leading technical design discussions.
• Cost optimization and cloud FinOps practices.

You'll Love

• Support teams building and deploying AI applications reliably and securely.
• Solve challenging cloud infrastructure, automation, and deployment problems.
• Work with modern cloud platforms, Kubernetes, infrastructure-as-code, and observability.
• Make a direct impact on platform reliability, developer velocity, and engineering efficiency.
• Collaborate with teams and help shape enterprise platform and DevOps best practices.
• Support teams building and deploying AI applications securely at scale.