DevOps Engineer (BigBrain)

Job Title: DevOps Engineer (BigBrain)

About monday.com

monday.com is a global software company transforming how businesses run. Our flexible platform enables diverse industries to reimagine work, increase efficiency, and scale with confidence. With over 2,500 employees and ~245,000 customers worldwide, we grow by prioritizing transparency and knowledge sharing, empowering our teams to take initiative, innovate, and make meaningful impact. We offer flexible work options, a supportive and collaborative work environment, and a culture focused on wellness, mental health, and employee growth.

About the Role

We are looking for a DevOps Engineer to join the BigBrain team and build next-generation, AI-powered products at massive scale. You will work alongside talented engineers and tech leads to design, implement, and support cloud infrastructure, build developer workflows, and manage production environments. As part of a fast-moving, startup-style team within monday.com, you will take end-to-end ownership, from architecture through production, partnering closely with product teams to drive innovation and operational excellence.

In this role, you will:

• Own and scale platform infrastructure — Manage multi-region Kubernetes clusters (EKS), streaming pipelines (Kafka/MSK, Debezium CDC), and data orchestration (Airflow) that handles billions of daily events.

• Build and operate AI infrastructure— Deploy and maintain the AI Gateway (governance, observability, guardrails for LLM usage), ML inference platform, and the tooling that enables AI adoption across the company.

• Drive infrastructure automation with AIOps** — Design and build autonomous agents and intelligent tooling (n8n, LangChain, Claude Code) that automate infrastructure operations, analyze workflows, and reduce manual toil.

• Build and maintain CI/CD & GitOps pipelines — Design and operate deployment pipelines with GitHub Actions, Argo CD, Helm, and Terraform (CDKTF), enabling fast, safe, and reliable releases for product teams.

• Ensure data security—Protect the company's most sensitive data through access controls, data governance, and security-first infrastructure design.

• Evolve the data platform— Work with modern data technologies (Snowflake, ClickHouse, Apache Iceberg, EMR) and contribute to the next generation of our data infrastructure.

• Enable developer self-service — Improve our internal platform so engineering teams across the company can deploy, configure, and operate services independently.

• Provide observability and reliability— Build monitoring, alerting, and debugging tools (Datadog, OpenTelemetry, ClickHouse) across all data and AI processes.

• Our Stack — AWS, Kubernetes (EKS), Kafka/MSK, Debezium, Airflow, Snowflake, ClickHouse, Apache Iceberg, EMR, ArgoCD, Terraform/CDKTF, Helm, GitHub Actions, Docker, Datadog, OpenTelemetry, MLFlow, n8n, API Gateway, Redis, MySQL, Teleport, TypeScript, Node.js, Python.

Requirements

• 3+ years of experience as a DevOps Engineer

• Strong technical skills and a good understanding of systems and infrastructure.

• Experienced with building the full application release cycle (CI/CD).

• Familiar with how modern web applications work and scale.

• Networking, firewall rules management, and application security knowledge.

• Familiar with Linux environment, scripting, and programming.

• Ability to see the bigger picture and carry out system architecture planning.

• Understanding of products and a passion for building software that impacts millions of users.

• Team player, egoless, strong communication skills, and empathetic.

• Big Data technologies such as Kafka/Flink - Advantage

Advantages

• Experience supporting AI-native products or LLM-based systems

• Familiarity with Cloudflare, PostgreSQL, and modern web app infrastructure

• Background in scalability, performance optimization, and cost management in cloud environments

• Experience in startup-like settings, rapid prototyping, or 0-to-1 product teams

• Track record of building internal developer platforms or tools, improving engineering productivity