Job descriptionWe are looking for a hands-on Senior / Lead DevOps Engineer to build, automate, and scale modern cloud-native platforms for GenAI, AI/ML, and production applications. This role will own CI/CD pipelines, Kubernetes-based deployments, platform reliability, observability, and secure production releases across development, staging, and production environments. Requirements Role Overview The role combines DevOps, platform engineering, and MLOps responsibilities, with a focus on deploying and maintaining AI/ML or GenAI workloads in production. Typical responsibilities in comparable roles include automating model and application deployment, integrating Kubernetes with CI/CD pipelines, monitoring production health, and improving scalability, reliability, and security.devsdata+2 Key Responsibilities Design, build, and maintain scalable CI/CD pipelines for application, API, and GenAI/ML workload deployments. Manage Kubernetes infrastructure across dev, test, staging, and production environments. Automate build, test, release, rollback, and deployment workflows using DevOps best practices. Deploy and support containerized services using Docker and Kubernetes. Enable production deployment of AI/ML models, GenAI applications, RAG pipelines, and related services.tiger-analytics. Implement monitoring, logging, alerting, and incident response for platform and production systems. Improve platform reliability, scalability, availability, and cost efficiency. Collaborate with software, data, AI/ML, and product teams to move solutions from development to production.jobs.welcome. Enforce security, access control, compliance, and infrastructure standards in cloud environments. Create technical documentation, deployment runbooks, and operational SOPs. Required Skills 4+ years of experience in DevOps, Platform Engineering, SRE, or MLOps-related roles. Strong hands-on experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps. Solid experience with Kubernetes, cluster operations, Helm, and container orchestration. Good knowledge of Docker, Linux, Bash/Shell, and Python scripting for automation. Experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation. Strong understanding of production deployment, release engineering, rollback strategies, and environment separation. Experience with observability tools such as Prometheus, Grafana, ELK, Datadog, or similar platforms. Familiarity with security best practices, IAM, secrets management, and vulnerability remediation. Preferred Skills Experience supporting GenAI, LLMOps, MLOps, or AI platform workloads in production. Familiarity with OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, or related AI tools/frameworks. Exposure to MLflow, Databricks, model registry workflows, and model lifecycle management. Experience with GitOps tools such as Argo CD or Flux. Understanding of model monitoring, drift detection, and AI service reliability. Lead-Level Expectations For a Lead title, the role should also include team guidance, architecture ownership, cross-functional coordination, and operational standards for deployment and platform reliability. Comparable lead roles also emphasize mentoring engineers, improving engineering processes, and driving scalable platform strategy across AI and production systems. Lead DevOps and platform architecture decisions for cloud-native and GenAI workloads. Mentor junior engineers and review infrastructure, automation, and deployment practices. Standardize CI/CD, release governance, observability, and security controls across teams. Partner with engineering and AI teams to improve delivery speed and production resilience.