AI Platform Engineer

This is a fully remote position, open to applicants in Costa Rica.

📋 Description

• Design, develop, and manage secure, scalable cloud platforms that empower client teams to deliver faster.

• Work closely with cross-functional product teams, including developers, DevOps engineers, security professionals, and product leaders.

• Minimize deployment friction and enhance platform reliability.

• Take ownership of platform deliverables and provide guidance on architectural decisions.

• Design and establish cloud infrastructure.

• Create deployment pipelines and operationalize observability.

• Contribute to platform security and compliance efforts.

• Deploy LLM applications and oversee vector databases for RAG systems.

• Secure agentic workflows and incorporate AI services into the platform architecture.

• Assist teams in adopting AI-assisted development tools and ensure their safe integration into platform workflows.

• Monitor model performance, prevent prompt injection attacks, ensure data governance, and reliably scale AI to production.

• Deploy, manage, and secure AI infrastructure, including LLM serving, vector stores, and agentic systems.

• Provide advice on AI deployment patterns and operational best practices during integration reviews.

• Design, implement, and maintain cloud architectures across development, staging, and production environments.

• Build and sustain deployment pipelines, CI/CD automation, and infrastructure-as-code.

• Implement observability systems using metrics, logging, and tracing for effective incident response.

• Manage cloud infrastructure provisioning, configuration, and security while maximizing reliability, cost-efficiency, and compliance.

• Monitor and enhance cloud resources for both cost and performance optimization.

• Collaborate with development, DevOps, and security teams to establish controls and best practices.

• Troubleshoot platform issues, engage in incident response, and drive improvements.

• Create runbooks, architecture documentation, and troubleshooting guides.

• Review infrastructure changes and contribute to architectural standards.

• Optimize systems for latency, throughput, and resource efficiency.

• Document solutions, share insights, and contribute to internal runbooks.

• Engage in pair programming and collaborate on complex technical challenges.

• Participate in code and architectural reviews.

⛳️ Requirements

• 3–8 years of hands-on experience in platform, infrastructure, DevOps, or cloud engineering.

• Familiarity with LLM APIs, vector databases, or AI model serving.

• Experience with AI-assisted development tools (GitHub Copilot, Claude, Cursor).

• Understanding of AI security aspects including prompt injection, data governance, model monitoring, and output validation.

• Strong expertise in supporting and building on one cloud platform (AWS, Azure, GCP).

• Extensive experience with infrastructure-as-code tools (Terraform, CloudFormation, Pulumi, etc.).

• Solid knowledge of Kubernetes, Docker, and CI/CD pipeline design.

• Practical experience with deployment automation, observability, and incident response.

• Ability to troubleshoot distributed systems and production issues; comfortable with on-call duties.

• Excellent communication skills; capable of explaining technical trade-offs to both technical and non-technical audiences.

• Proficiency in scripting languages (Python, Bash, Go) and foundational Linux/Unix administration skills.

• Familiarity with version control (Git), collaboration workflows, and infrastructure automation.

• Availability for regular working sessions and discovery workshops with client teams.

• Daily overlap with client business hours.

• Reliable high-speed internet connection.

• Ability to work independently while collaborating with client and Modus leadership.

• Capacity to coordinate with security, product, and other teams beyond infrastructure and development.

🏝️ Benefits

• Fully remote work environment.

• A collaborative and innovative atmosphere.

• Opportunities to provide strategic advice to leadership on platform direction and strategy.

• Opportunities to assist teams in building and deploying AI applications in a reliable and secure manner.

• Opportunities to engage with modern cloud platforms, Kubernetes, infrastructure-as-code, and observability tools.

• Opportunities to directly impact platform reliability, developer speed, and engineering effectiveness.

• Collaborate with teams to shape enterprise platform and DevOps best practices.

• Regular working sessions and discovery workshops with client teams.

• Daily overlap with client business hours.

• Reliable high-speed internet support/requirement.

• Team collaboration through pair programming, code reviews, architecture reviews, and shared learning experiences.

• Be part of a winning team that celebrates every achievement together.