DevOps Engineer

Job Overview

Resonance is a technology company building a more sustainable and valuable fashion industry for designers, brands, manufacturers, consumers, and the planet. The company’s AI-powered operating system, ONE, enables brands to design, sell, and make in that order – empowering designers to operate with no unnecessary inventory and eliminating the financial and environmental burdens of the legacy fashion industry. Resonance ONE is our end-to-end platform that powers every aspect of an apparel brand’s business, constantly learning and optimizing how garments are designed, sold, and made.

About the Role

We’re looking for a talented DevOps Engineer to join our remote team and help scale the sophisticated infrastructure behind Resonance ONE. As a DevOps Engineer at Resonance, you will play a critical role in designing, building, and maintaining a complex full-stack platform that underpins everything from digital design tools to e-commerce and manufacturing automation. Our stack spans a wide range of modern technologies – from machine learning services (OpenAI and other ML models) to a robust cloud backend (AWS infrastructure, AWS Lambda), data and analytics systems (Hasura GraphQL engine, Snowflake data warehouse, Looker BI), event streaming (Kafka), and orchestration tools (Kubernetes with Argo Workflows, plus integrations with tools like Airtable) – all working in concert to realize our mission.

In this role, you will ensure these diverse components work together in harmony, securely and at scale. You’ll have the opportunity to shape and implement scalable DevOps practices and systems from the ground up in a forward-thinking, AI-driven organization. You will collaborate closely with software engineers, data scientists, and product teams to continuously improve our development pipeline, deployment processes, and infrastructure automation. This is a unique chance to tackle challenging problems in an architecture that pushes the boundaries of technology – all while enabling fashion brands to innovate without waste.

Key Responsibilities

• Architect and Maintain Cloud Infrastructure: Build, maintain, and scale our AWS cloud infrastructure using infrastructure-as-code and modern CI/CD pipelines (e.g. Argo Workflows). Ensure reliable, automated deployments of our applications and machine learning services across development, staging, and production environments.


• Container Orchestration: Manage our Kubernetes clusters and containerized microservices, optimizing for high availability, security, and efficient resource usage. Continuously improve our cluster deployment, scaling strategies, and rollback processes to support a rapidly growing platform.


• CI/CD & Automation: Design and implement continuous integration and delivery pipelines that empower our development team to ship code and ML model updates quickly and safely. Automate routine operations and workflows, reducing manual work through scripts, AWS Lambda functions, and other automation tools.


• Monitoring & Reliability: Implement robust monitoring, logging, and alerting (using tools like Prometheus, CloudWatch, etc.) to proactively track system performance and reliability. Quickly troubleshoot and resolve infrastructure issues or bottlenecks across the stack to maintain high uptime and responsive services.


• Data & Pipeline Integration: Work closely with our data engineering team to support a seamless flow of data through the platform. Maintain and optimize our event streaming and pipeline architecture (Kafka) and its integration with downstream systems like our Snowflake data warehouse and Looker analytics, ensuring data is delivered accurately and on time.


• AI/ML Infrastructure: Collaborate with machine learning engineers to deploy and scale AI/ML models in production. Support the integration of OpenAI and other ML models into our applications, implementing the infrastructure (compute, storage, containers) needed for model training, inference, and monitoring model performance in a live environment.


• Tool Integration & Support: Integrate and manage internal and third-party tools that extend our platform’s functionality – for example, maintaining our Hasura GraphQL engine that interfaces with databases, or automating workflows involving external services like Airtable. Ensure these tools are properly deployed, updated, and aligned with our security and compliance standards.


• DevOps Best Practices & Culture: Champion DevOps best practices across the engineering organization. This includes improving our release processes (e.g. implementing GitOps workflows), optimizing build/test pipelines, and mentoring developers on using infrastructure tools. You will continually evaluate new technologies and processes to enhance deployment speed, reliability, and scalability, while balancing rapid iteration with operational stability.

Required Qualifications

Minimum Requirements

Experience: 5+ years of experience in DevOps, SRE, or related infrastructure engineering roles, with a track record of managing complex, distributed systems at scale.

Cloud Proficiency: Strong expertise in AWS and cloud architecture (compute, storage, networking, and security). You have designed and maintained scalable infrastructure using services like EC2/ECS/EKS, S3, RDS, VPC, and Lambda, and you understand how to build secure and cost-efficient cloud environments.

Containers & Orchestration: Hands-on experience with containerization and orchestration – you have managed production-grade Kubernetes clusters and containerized applications, with experience in tools like Docker, Kubernetes, and orchestration tools like Argo Workflows.

CI/CD & Automation: Proven ability to design and implement CI/CD pipelines and automation workflows, using tools like Jenkins, GitLab CI, or similar. Experience with infrastructure-as-code tools (Terraform, CloudFormation) and scripting languages (Python, Bash) is essential.

Monitoring & Reliability: Experience implementing comprehensive monitoring, logging, and alerting systems to ensure system reliability and performance. Familiarity with tools like Prometheus, Grafana, ELK stack, or AWS CloudWatch is required.

Data & Event Streaming: Understanding of data pipeline architectures and event streaming technologies, with experience working with tools like Kafka, Kinesis, or similar.

AI/ML Infrastructure: Experience supporting machine learning workflows, including deploying and scaling ML models in production environments. Familiarity with ML platforms and tools is a plus.

Collaboration & Communication: Strong ability to collaborate with cross-functional teams, including software engineers, data scientists, and product managers. Excellent communication skills to document processes and explain complex technical concepts to non-technical stakeholders.

Problem-Solving & Troubleshooting: A proactive approach to identifying and resolving technical challenges, with strong debugging and root cause analysis skills.