DevOps Engineer

Job Title : Senior DevOps Engineer

Location : Remote

Experience Level : 5+ Years

Role Overview :

We are a funded AI startup seeking a Senior DevOps Engineer to design, implement, and maintain a secure, scalable, and efficient infrastructure. In this role, you will focus on automating operations, optimizing deployment processes, and enabling engineering teams to deliver high-quality products seamlessly.

Key Responsibilities:

Infrastructure Scalability & Reliability :

• Architect and manage cloud infrastructure on AWS, GCP, or Azure for high availability, reliability, and cost-efficiency.
• Implement container orchestration using Kubernetes or Docker Compose.
• Utilize Infrastructure as Code (IaC) tools like Pulumi or Terraform to manage and configure infrastructure.

Deployment Automation :

• Design and maintain CI/CD pipelines using GitHub Actions, Jenkins, or similar tools.
• Implement deployment strategies such as canary or blue-green deployments, and create rollback mechanisms to ensure seamless updates.

Monitoring & Observability :

• Leverage tools like OpenTelemetry, Grafana, and Datadog to monitor system health and performance.
• Establish centralized logging systems and create real-time dashboards for actionable insights.

Security & Compliance :

• Securely manage secrets using tools like HashiCorp Vault or Doppler.
• Conduct static code analysis with tools such as SonarQube or Snyk to ensure compliance with security standards.

Collaboration & Team Enablement :

• Mentor and guide team members on DevOps best practices and workflows.
• Document infrastructure setups, incident runbooks, and troubleshooting workflows to enhance team efficiency.

Required Skills :

• Expertise in managing cloud platforms like AWS, GCP, or Azure.
• In-depth knowledge of Kubernetes, Docker, and IaC tools like Terraform or Pulumi.
• Advanced scripting capabilities in Python or Bash.
• Proficiency in CI/CD tools such as GitHub Actions, Jenkins, or similar.
• Experience with observability tools like Grafana, OpenTelemetry, and Datadog.
• Strong troubleshooting skills for debugging production systems and optimizing performance.

Preferred Qualifications :

• Experience in scaling AI or ML-based applications.
• Familiarity with distributed systems and microservices architecture.
• Understanding of agile methodologies and DevSecOps practices.
• Certifications in AWS, Azure, or Kubernetes.

What We Offer :

• Opportunity to work in a fast-paced AI startup environment.
• Flexible remote work culture.
• Competitive salary and equity options.
• Professional growth through challenging projects and learning opportunities.

🚀 Hiring: Azure DevOps Engineer – Immediate Joiners Only! 🚀

📍 Location: Pune (Hybrid)

💼 Experience: 5+ Years

🕒 Mode of Work: Hybrid

Are you a proactive and skilled Azure DevOps Engineer looking for your next challenge? We are hiring immediate joiners to join our dynamic team! If you are passionate about CI/CD, cloud automation, and SRE best practices, we want to hear from you.

🔹 Key Skills Required:

✅ Cloud Expertise: Proficiency in any cloud (Azure preferred)

✅ CI/CD Pipelines: Hands-on experience in designing and managing pipelines

✅ Containers & IaC: Strong knowledge of Docker, Terraform, Kubernetes

✅ Incident Management: Quick issue resolution and RCA

✅ SRE & Observability: Experience with SLI/SLO/SLA, monitoring, tracing, logging

✅ Programming: Proficiency in Python, Golang

✅ Performance Optimization: Identifying and resolving system bottlenecks

The DevOps Engineer will play a critical role in operationalizing artificial intelligence across Bell Techlogix client environments. This role focuses on building and supporting cloud infrastructure, CI/CD pipelines, and automation frameworks that power AI and machine learning workloads. The ideal candidate has experience supporting AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, and ServiceNow or conversational AI platforms, and understands the operational requirements of production AI systems, including reliability, scalability, and security. 

Key Responsibilities 

•Design, build, and operate cloud infrastructure and platform services that support AI and machine learning workloads in production, SLA-driven managed services environments 

•Implement CI/CD and MLOps pipelines to enable automated training, testing, deployment, and rollback of AI and ML models 

•Develop and maintain Infrastructure as Code to provision AI-ready environments consistently across dev/test/prod 

•Support AI platform operations including monitoring model health, pipeline execution, compute utilization, and data dependencies 

•Partner with Machine Learning Engineers and Data Engineers to standardize deployment patterns for AI services and LLM-based solutions 

•Enable secure and scalable AI integrations using APIs, messaging, and event-driven architectures 

•Implement observability solutions for AI platforms, including logging, metrics, alerting, and drift detection integrations 

•Troubleshoot AI platform incidents, perform root cause analysis, and implement remediation to improve reliability and automation coverage 

•Apply security best practices for AI environments including secrets management, identity and access controls, network isolation, and policy enforcement 

•Support AI-driven automation use cases across platforms such as Microsoft Copilot, ServiceNow, and conversational AI tools 

•Collaborate with service desk, security, and architecture teams to continuously improve AI service delivery and operational maturity 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience 

•5+ years of experience in DevOps, cloud engineering, or platform operations, with exposure to AI or data workloads 

•Hands-on experience with Microsoft Azure, including compute, networking, storage, and monitoring services 

•Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools 

•Working knowledge of Infrastructure as Code (Terraform and/or Bicep/ARM) 

•Scripting experience using PowerShell and/or Python 

•Experience supporting production platforms with incident management, change control, and root cause analysis 

•Understanding of cloud security fundamentals and enterprise governance requirements 

Preferred Qualifications 

•Experience with Azure Machine Learning, Azure AI Services, Azure OpenAI, or MLOps frameworks 

•Exposure to containerization and orchestration technologies (Docker, Kubernetes, AKS) 

•Experience supporting data pipelines or feature stores used by machine learning systems 

•Familiarity with ServiceNow, AI-driven ITSM workflows, or automation platforms 

•Experience with observability tools 

•Knowledge of Responsible AI, data governance, and compliance considerations for AI systems 

•Relevant certifications (Microsoft Azure Administrator, Azure DevOps Engineer, Azure AI Engineer)

Key Responsibilities:

• ☁️ Manage cloud infrastructure and automation on AWS, Google Cloud (GCP), and Azure.
• 🖥️ Deploy and maintain Windows Server environments, including Internet Information Services (IIS).
• 🐧 Administer Linux servers and ensure their security and performance.
• 🚀 Deploy .NET applications (ASP.Net, MVC, Web API, WCF, etc.) using Jenkins CI/CD pipelines.
• 🔗 Manage source code repositories using GitLab or GitHub.
• 📊 Monitor and troubleshoot cloud and on-premises server performance and availability.
• 🤝 Collaborate with development teams to support application deployments and maintenance.
• 🔒 Implement security best practices across cloud and server environments.

Required Skills:

• ☁️ Hands-on experience with AWS, Google Cloud (GCP), and Azure cloud services.
• 🖥️ Strong understanding of Windows Server administration and IIS.
• 🐧 Proficiency in Linux server management.
• 🚀 Experience in deploying .NET applications and working with Jenkins for CI/CD automation.
• 🔗 Knowledge of version control systems such as GitLab or GitHub.
• 🛠️ Good troubleshooting skills and ability to resolve system issues efficiently.
• 📝 Strong documentation and communication skills.

Preferred Skills:

• 🖥️ Experience with scripting languages (PowerShell, Bash, or Python) for automation.
• 📦 Knowledge of containerization technologies (Docker, Kubernetes) is a plus.
• 🔒 Understanding of networking concepts, firewalls, and security best practices.

Requirements

Core skills:

● Strong background in Linux / Unix Administration and

troubleshooting

● Experience with AWS (ideally including some of the following:

VPC, Lambda, EC2, Elastic Cache, Route53, SNS, Cloudwatch,

Cloudfront, Redshift, Open search, ELK etc.)

● Experience with Infra Automation and Orchestration tools

including Terraform, Packer, Helm, Ansible.

● Hands on Experience on container technologies like Docker,

Kubernetes/EKS, Gitlab and Jenkins as Pipeline.

● Experience in one or more of Groovy, Perl, Python, Go or

scripting experience in Shell.

● Good understanding of with Continuous Integration(CI) and

Continuous Deployment(CD) pipelines using tools like Jenkins,

FlexCD, ArgoCD, Spinnaker etc

● Working knowledge of key value stores, database technologies

(SQL and NoSQL), Mongo, mySQL

● Experience with application monitoring tools like Prometheus,

Grafana, APM tools like NewRelic, Datadog, Pinpoint

● Good exposure on middleware components like ELK, Redis, Kafka

and IOT based systems including Redis, NewRelic, Akamai,

Apache / Nginx, ELK, Grafana, Prometheus etc

Good to have:

● Prior experience in Logistics, Payment and IOT based applications

● Experience in unmanaged mongoDB cluster, automations &

operations, analytics

● Write procedures for backup and disaster recovery

Core Experience

● 3-5 years of hands-on DevOps experience

● 2+ years of hands-on Kubernetes experience

● 3+ years of Cloud Platform experience with special focus on

Lambda, R53, SNS, Cloudfront, Cloudwatch, Elastic Beanstalk,

RDS, Open Search, EC2, Security tools

● 2+ years of scripting experience in Python/Go, shell

● 2+ years of familiarity with CI/CD, Git, IaC, Monitoring, and

Logging tools

1. Develop and Deploy Software:
1. Architect and create an effective build and release process using industry best practices and tools
2. Create and manage build scripts to deploy software in a multi-cloud environment
3. Look for opportunities to automate as much of the deployment process as possible to provide for repeatability, auditability, scalability and build in process enforcement
2. Manage Release Schedule:
1. Act as a “gate keeper” for all releases into production
2. Work closely with business stakeholders, development managers and developers to prepare a release schedule
3. Help prioritize deployment requests for version upgrades, patches and hot-fixes
3. Continuous Delivery of Software:
1. Implement Continuous Integration (CI) practices to drive development teams to implement smaller changes and commit code to the version control repo frequently
2. Implement Continuous Development (CD) practices that automates deployment of the application to several environments – Dev, Test and Production
3. Implement Continuous Testing (functional and non-functional) to execute tests in the CI/CD pipeline
4. Manage Version Control:
1. Define and implement branching policies to efficiently manage source-code
2. Implement business rules as a part of source control standards
5. Resolve Software Issues:
1. Assist technical support and development teams to troubleshoot issues and identify areas that need improvement
2. Address deployment related issues
6. Maintain Release Documentation:
1. Maintain release notes (features available in stable versions and known issues) and other documents for both internal and external end users

A strong background in Azure OR Amazon Web Services (AWS) or a similar cloud platform is a must-have, certification is a plus.

Excellent technical skills and knowledge include but not limited to: cloud methodologies like PaaS and SaaS; programming languages such as Python, Java, .Net; orchestration systems such as Chef, Ansible, Terraform; Azure IaaS servers; PowerShell scripting.

Fully aware of the DevOps cycle with hands-on on deployment models to the cloud.

Experience working with Docker and related containerization technologies.

Experience working on orchestration platforms such as AKS, RedHat OpenShift etc.

Extensive knowledge working with logging and monitoring tools such as EFK, visualization tools such as Grafana and Prometheus.

Exposure to security alert monitoring tools.