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 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.
By joining our team, you'll be part of a winning squad that plays to each other's strengths and celebrates every success together.
