We are looking for a highly hands-on DevOps Engineer with 3+ years of experience to take strong ownership of a growing DevOps ecosystem end-to-end. This is systems-heavy engineering, not standard infrastructure maintenance — the role covers architecting cloud-native distributed systems, scaling high-throughput analytics pipelines, building enterprise-grade SaaS and on-prem deployments, and driving DevOps excellence across the company. If you enjoy owning infrastructure like a product, this role will suit you well.

Key Responsibilities

Architect and manage scalable infrastructure using AWS services; design and maintain a multi-tenant SaaS architecture handling millions of real-time events
Own and enhance CI/CD pipelines using GitHub Actions; build automated deployments for microservices, backend systems, UI applications, and browser extensions
Implement Infrastructure-as-Code using Terraform; build and maintain Dockerized, container-orchestrated environments
Implement end-to-end observability (logs, metrics, traces), alerting, and incident-response systems; ensure high availability, auto-scaling, disaster recovery, and backup strategies
Drive security best practices — network isolation, IAM policies, CVE remediation, container security
Design and deliver on-premise versions of the SaaS platform, replicating cloud architecture in constrained enterprise environments, and automating installation, upgrades, and maintenance
Collaborate closely with Engineering, QA, and Product teams; contribute to architecture and design decisions; act as a technical owner for DevOps initiatives
Use AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) as a primary part of daily infrastructure and automation work
Write and maintain specs/rules files (e.g. CLAUDE.md, .cursorrules, agent skill files) that direct AI coding tools for the DevOps and engineering teams
Design and build AI agents and agentic workflows for infra automation, incident response, and operational tooling using LLMs (OpenAI, Claude, Gemini, or similar)
Practice spec-driven development — translate infrastructure and platform requirements into clear specs that guide both human and AI-driven implementation

Mandatory Required Skills

3+ years in DevOps with hands-on ownership of production systems
Strong AWS expertise (mandatory), including Kubernetes (EKS), VPC, EC2, RDS, ElastiCache, S3, CloudFront, Route 53, API Gateway, Lambda, and Kinesis
Experience with ClickHouse or similar columnar databases, Docker and container orchestration, and Helm charts
Strong GitHub Actions experience and Terraform expertise (mandatory)
Strong scripting skills in Python, Shell, and Node.js
Experience with high-scale distributed systems — load balancing, caching strategies, event-driven architectures, high-throughput pipelines
Experience building SaaS-to-on-prem deployments and automating complex enterprise setups
Hands-on experience with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) as a core part of daily engineering workflow

Preferred

Experience with Digital Adoption Platforms or browser-based products
Experience with monitoring tools such as Grafana, Prometheus, or Datadog
Experience designing and building AI agents / agentic workflows using LLMs (OpenAI, Claude, Gemini, or similar) for automation, tooling, or operational use cases
Familiarity with spec-driven development practices and maintaining AI tool configuration/rules files (e.g. CLAUDE.md, .cursorrules, agent skill files) for a team

Nice to Have

Exposure to SOC 2 or ISO compliance environments
Development experience beyond scripting (full application development background)

Qualifications

Degree in Computer Science, Engineering, or a related field preferred (to be confirmed with client)

Experience Required

3+ years in DevOps with demonstrated, hands-on ownership of production infrastructure — not solely pipeline maintenance.

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