Cloud & DevOps Engineer (Infrastructure Platform)

Title : Cloud & DevOps Engineer (Infrastructure Platform)

Work Location : Bangalore

Job Type : Full time

Work Type: Onsite-Monda-Friday

Shift: UK Shift - 1:30 PM to 10:30 PM IST

Overview:

TekWissen is a global workforce management provider throughout India and many other countries in the world.

Job Description:

• We are seeking a Cloud, DevOps & MLOps Engineer with strong hands-on experience in cloud infrastructure, automation, CI/CD, container platforms, and machine learning platform operations.
• This role requires professionals who can own cloud environments end-to-end while also supporting AI/ML workloads, model deployment pipelines, and scalable AI infrastructure.
• The ideal candidate brings practical production experience in DevOps practices and ML platform enablement, strong troubleshooting skills, and the ability to improve operational maturity across cloud, DevOps, and MLOps practices.
• The role involves collaboration with data scientists, ML engineers, and application teams to enable scalable and reliable AI-powered solutions.

Key Responsibilities:

Cloud Infrastructure Ownership:

• Design, provision, and manage infrastructure workloads across AWS, Azure, or GCP environments
• Own lifecycle management of compute, networking, storage, and platform services
• Support infrastructure required for AI/ML training, inference, and data pipelines
• Manage compute environments including GPU/accelerated workloads for machine learning
• Ensure infrastructure availability, scalability, and operational stability
• Implement infrastructure standards, templates, and reusable deployment patterns

Infrastructure as Code & Automation

• Develop and maintain infrastructure using Terraform or similar IaC tools
• Automate provisioning of environments for data science and ML experimentation
• Automate provisioning, configuration, and deployment workflows

CI/CD & Release Enablement

• Design and maintain robust CI/CD pipelines using GitHub Actions, GitLab CI, Azure DevOps, or Jenkins
• Enable ML model CI/CD pipelines (MLOps) for model versioning, validation, and deployment
• Automate build, test, security scan, and deployment pipelines for both applications and ML models
• Enable automated build, test, security scan, and deployment pipelines

Containerization & Kubernetes

• Build, deploy, and manage containerized applications using Docker
• Support Kubernetes clusters for microservices and ML inference workloads
• Manage scalable deployment of AI model APIs

ML Platform Support

• Support infrastructure for machine learning workflows and model lifecycle
• Enable model training, experiment tracking, and model deployment pipelines
• Collaborate with data scientists and ML engineers to operationalize models
• Support frameworks such as: (MLFlow , Kubeflow, Azure ML, SageMaker)

System Administration & Platform Reliability

• Manage Linux / Windows server environments including patching, performance tuning, and security hardening
• Support high availability environments for AI applications and data pipelines
• Participate in incident response, root cause analysis, and resolution activities
• Improve monitoring, alerting, and operational readiness practices
• Maintain documentation for infrastructure and operational runbooks

Security & Access Management

• Implement IAM policies, RBAC controls, and secure access models
• Secure ML pipelines and data access
• Ensure secure handling of secrets, certificates, and credentials

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or related field
• 6-12 years of experience in Cloud Engineering, DevOps, Infrastructure Engineering, or Platform Support roles
• Strong hands-on experience with at least one public cloud (AWS / Azure / GCP)
• Proven experience implementing Infrastructure as Code using Terraform
• Experience building and maintaining CI/CD pipelines
• Hands-on exposure to Docker and Kubernetes environments
• Strong scripting skills (Bash / Python / PowerShell)
• Understanding of cloud infrastructure for AI workloads

Preferred Experience

• Experience supporting multi-region or multi-environment cloud deployments
• Exposure to cloud monitoring tools such as CloudWatch, Azure Monitor, Prometheus, Grafana
• Understanding of model deployment pipeline
• Experience with vector databases or AI workloads
• Understanding of cost optimization and cloud governance practices
• Experience working in global delivery or production support environments
• Exposure to platform engineering or SRE practices

Certifications (Preferred)

• AWS Associate / Azure Administrator / GCP Associate Cloud Engineer
• Terraform Associate Certification
• Kubernetes and Cloud Native Associate (KCNA) or CKA
• CompTIA Security+
• Linux Foundation Certification (LFCS / LFCE)

Key Competencies:

• Strong ownership mindset and execution discipline
• Ability to troubleshoot complex infrastructure issues
• Structured thinking and documentation capability
• Collaboration with distributed global teams
• Continuous learning and improvement mindset

Work Environment:

• Structured office-based engineering collaboration
• Exposure to AI platforms, ML pipelines, and production AI deployments
• Participation in incident troubleshooting and operational reviews
• Adherence to enterprise security and compliance standards

TekWissen Group is an equal opportunity employer supporting workforce diversity.