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As a Lead of DevOps & Cloud Engineering at Guidepoint, you will play a crucial role in managing and leading a team of Sys. Admin and DevOps engineers. Your primary responsibility will be to develop and execute a robust Development Operations (DevOps) strategy to ensure high-quality software deployments and maintain the overall health and performance of applications. This position is based in our Pune office and offers a hybrid work environment. Your key responsibilities will include: - Taking complete ownership of DevOps/DevSecOps pipelines to support Enterprise Production, Development, and Test deployments - Managing CI/CD pipelines using tools such as Azure DevOps, Argo CD, Keda, and Velero for backups and disaster recovery - Leading and overseeing a team of experienced DevOps engineers across global teams - Provisioning, configuring, optimizing, and supporting all components of the Web Applications Stack, including Linux, Apache, SQL, PHP, Solr, ElasticSearch, Redis, and Couchbase - Right-sizing and optimizing infrastructure for AKS and Azure VMs to balance performance and cost - Securing infrastructure by applying patches and updates regularly - Configuring and managing monitoring and alert systems for Infrastructure and Application - Providing production support by troubleshooting and triaging issues - Contributing to overall engineering initiatives as part of the engineering leadership team To be successful in this role, you should have: - 5+ years of experience as a Senior DevOps Engineer - 3+ years of experience in managing a team of DevOps/DevSecOps Engineers - Expertise in CI/CD tools like Terraform, Ansible, Azure DevOps Pipelines, Argo CD, Keda, and Velero - Hands-on experience with various technologies including Linux, PHP, Apache, Nginx, Solr, Elastic Search Clusters, Redis, Couchbase, SQL Server, RabbitMQ, and git version control - Knowledge of Datadog for Infrastructure and Application monitoring - Preferred experience with IaaS and PaaS Cloud services (Azure), Containerization Technology (Docker, Kubernetes, ASR), and Azure IaaS/PaaS components In addition to technical skills, we are looking for candidates with strong interpersonal and communication skills, problem-solving abilities, and a proactive work style. You should be a collaborative team player, exhibit effective leadership skills, and be able to manage projects while upholding quality standards and meeting deadlines. Guidepoint offers competitive compensation, employee medical coverage, a central office location, an entrepreneurial environment, autonomy, and a casual work setting. Join Guidepoint, a leading research enablement platform, and be part of a diverse and inclusive community that values different perspectives and contributions.,

Key Responsibilities: Deliver engaging and interactive training sessions (24 hours total) based on structured modules. Teach integration of monitoring, logging, and observability tools with machine learning. Guide learners in real-time anomaly detection, incident management, root cause analysis, and predictive scaling. Support learners in deploying tools like Prometheus, Grafana, OpenTelemetry, Neo4j, Falco, and KEDA. Conduct hands-on labs using LangChain, Ollama, Prophet, and other AI/ML frameworks. Help participants set up smart workflows for alert classification and routing using open-source stacks. Prepare learners to handle security, threat detection, and runtime anomaly classification using LLMs. Provide post-training support and mentorship when necessary. Observability & Monitoring: Prometheus, Grafana, OpenTelemetry, ELK Stack, FluentBit AI/ML: Python, scikit-learn, Prophet, LangChain, Ollama (LLMs) Security Tools: Falco, KubeArmor, Sysdig Secure Dev Tools: Docker, VSCode, Jupyter Notebooks LLMs & Automation: LangChain, Neo4j, GPT-based explanation tools, Slack Webhooks.

Key Responsibilities: Deliver engaging and interactive training sessions (24 hours total) based on structured modules. Teach integration of monitoring, logging, and observability tools with machine learning. Guide learners in real-time anomaly detection, incident management, root cause analysis, and predictive scaling. Support learners in deploying tools like Prometheus, Grafana, OpenTelemetry, Neo4j, Falco, and KEDA. Conduct hands-on labs using LangChain, Ollama, Prophet, and other AI/ML frameworks. Help participants set up smart workflows for alert classification and routing using open-source stacks. Prepare learners to handle security, threat detection, and runtime anomaly classification using LLMs. Provide post-training support and mentorship when necessary. Skills Kubernetes: Minikube, Helm, kubectl, HPA, KEDA Observability & Monitoring: Prometheus, Grafana, OpenTelemetry, ELK Stack, FluentBit AI/ML: Python, scikit-learn, Prophet, LangChain, Ollama (LLMs) Security Tools: Falco, KubeArmor, Sysdig Secure Dev Tools: Docker, VSCode, Jupyter Notebooks LLMs & Automation: LangChain, Neo4j, GPT-based explanation tools, Slack Webhooks

The tackling challenges to enhance our internal CRM system and develop new products that You'llontakesproblem-solvingjudgmentupholds Overview: Guidepoints Engineering team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal CRM system and create new products to optimize the seamless delivery of our services. As a Lead of DevOps & Cloud Engineering, you will be a core member of the engineering leadership team. In this role, you will manage and lead a team of Sys. Admin and DevOps engineers. You will be responsible for developing and executing a Development Operations (DevOps) strategy to ensure quality software deployments and overall application health and performance This is a hybrid position out of our Pune office. What Youll Do: Complete ownership of DevOps/DevSecOps pipelines, supporting Enterprise Production, Development, and Test deployments Managing CI/CD pipelines using Azure DevOps, Argo CD, Keda, and Velero for backups and DR Lead and manage a team of experienced DevOps engineers in our global team Provision, install, configure, optimize, maintain, and support all components of the Web Applications Stack: (Linux, Apache, SQL, PHP, Solr, ElasticSearch, Redis, Couchbase) Right size and optimize infrastructure, balancing performance and cost for AKS and Azure VMs Secure all infrastructure by applying patches and updates at regular intervals Configure and manage to monitor and alert of issues with Infrastructure and Application Provide production support by triaging and troubleshooting Contribute to overall engineering initiatives as a member of the engineering leadership team What You Have: 5+ years of experience working as a Senior DevOps Engineer 3+ years of experience managing a team of DevOps/DevSecOps Engineers Datadog experience with Infrastructure and Application monitoring and observability Tools Solid understanding and hands-on experience with CI/CD tools: Terraform, Ansible Azure DevOps Pipelines, Argo CD Keda and Velero expertise Hands-on experience and knowledge of Linux (CentOS), PHP (configs/modules), Apache, Nginx, Solr, Elastic Search Clusters, Redis, Couchbase, SQL Server, and RabbitMQ. Well-versed in git version control and Artifactory Soft Skills Required: Maintain problem solving skills and a proactive work style Strong interpersonal & communication skills Independent contributor with drive Collaborative and teamwork-oriented Accountable for work and take ownership of tasks Strategic thinker Manages projects and uphold quality on a deadline Exhibits judgement when it comes to prioritization and overall team management Proven effective leadership skills What We Offer: Competitive compensation Employee medical coverage Central office location Entrepreneurial environment, autonomy, and fast decisions Casual work environment

Job Summary: We are seeking a versatile QA & AIOps Engineer with a minimum of 5 years of hands-on experience in both AI Ops implementation and intelligent QA automation. The ideal candidate will bridge the gap between DevOps, QA, and AI engineering ensuring robust infrastructure, AI agent deployment, intelligent testing, and automated monitoring for scalable AI systems. This role requires strong technical depth in cloud-native deployment (AKS/EKS), CI/CD pipelines, and AI/ML toolchains. Key Responsibilities: AI Ops & DevOps Engineering: Deploy and manage AI agents in scalable production environments (Azure/AWS). Set up and maintain Kubernetes clusters (AKS/EKS), Docker containers, and Ingress Controllers (NGINX/Traefik). Integrate platforms like Azure AI Foundry or AWS Bedrock into production pipelines. Implement auto-scaling policies using tools like KEDA or Horizontal Pod Autoscaler. Promote use of Vector Databases (e.g., Pinecone, Weaviate) for AI workflows. Configure infrastructure security: secrets management, access controls, and network policies. Use Helm charts to manage complex deployments and upgrades of AI services. Troubleshoot production issues, identify bottlenecks, and ensure high availability of AI workloads. Collaborate with SREs, DevOps, and ML engineers to ensure seamless agent deployment, monitoring, and scaling. QA & Intelligent Testing: Design, develop, and execute test cases (manual and automated) for web, API, and backend systems. Implement AI-driven test strategies (e.g., test prioritization, risk-based testing). Create and maintain automated test scripts using tools like JUnit, PyTest, and JMeter. Integrate QA automation into CI/CD pipelines with anomaly detection and predictive analytics. Log and manage bugs; track issues to closure with development and DevOps teams. Use AI-powered QA tools such as Test.ai, Functionize for test optimization. Monitor AIOps dashboards to validate intelligent issue detection and auto-remediation flows. Required Skills & Qualifications: 5+ years of combined experience in QA automation, DevOps, or AI Ops roles. Hands-on expertise with Kubernetes (AKS/EKS), Docker, Helm, CI/CD pipelines. Experience integrating AI/ML tools such as Azure AI Foundry, AWS Bedrock. Knowledge of AI Ops platforms, observability tools, and monitoring dashboards. Proficient in Python, Bash, or JavaScript. Familiarity with Vector DBs, AI observability, and security practices. Strong collaboration skills with cross-functional DevOps, QA, and AI engineering teams.

Role Overview: We're seeking a senior Azure Infrastructure Engineer with 812 years of deep hands-on experience in building, deploying, and operating cloud-native infrastructure. You’ll be responsible for core components like AKS, Terraform, Docker, Helm, KEDA, HPA, Istio/service mesh, CI/CD pipelines, Azure networking, and disaster recovery. Key Responsibilities: Operate and troubleshoot production AKS clusters Build and deploy workloads using Docker and Helm Automate infra provisioning with Terraform Configure autoscaling using KEDA and HPA Manage Istio or equivalent service mesh (ingress, routing, mTLS) Maintain robust CI/CD pipelines (Azure DevOps/GitHub Actions) Handle complex Azure networking (VNet, NSG, DNS, LB, peering) Support and execute disaster recovery procedures Required Skills: 8–12 years in infrastructure/DevOps roles with deep expertise in: Azure, AKS, Docker, Terraform, Helm KEDA, HPA, Istio/service mesh CI/CD pipelines, Linux, Bash/PowerShell scripting Azure networking and disaster recovery

Key Responsibilities: Deliver engaging and interactive training sessions (24 hours total) based on structured modules. Teach integration of monitoring, logging, and observability tools with machine learning. Guide learners in real-time anomaly detection, incident management, root cause analysis, and predictive scaling. Support learners in deploying tools like Prometheus, Grafana, OpenTelemetry, Neo4j, Falco, and KEDA. Conduct hands-on labs using LangChain, Ollama, Prophet, and other AI/ML frameworks. Help participants set up smart workflows for alert classification and routing using open-source stacks. Prepare learners to handle security, threat detection, and runtime anomaly classification using LLMs. Provide post-training support and mentorship when necessary.