Remote Senior python developer Jobs – 111 Open Jobs Today

AI Devops Engineer - CST Time Zone
Ekshvaku Tech Innovation —Remote
- Full-time
- Remote
- DevOps
- AWS
- Scripting
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FuGenEd ( Creating Opportunities For Deserving) —Remote
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- Full-time
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- Azure
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Luxoft —Remote
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SACMIA Pvt. Ltd. —Remote
- Email
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- ERP systems
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- Work from home
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House Of Edtech —Remote
- From ₹5,00,000 a year
- Full-time
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AI Devops Engineer - CST Time Zone

Ekshvaku Tech Innovation
Remote

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Job details

Full-time

Qualifications

CI/CD
Cloud infrastructure
Go
Kubernetes
Ansible
DevOps
Git
Bash (Unix shell)
AWS
Docker
Distributed systems
Terraform
Continuous integration
New Relic
Scripting
GitHub
ISO 27001
AI
Communication skills
Python
AWS Certified DevOps Engineer – Professional
Identity & access management

Full job description

Please find the Job Description (JD) attached for your reference.

Senior DevOps Engineer
Location: Remote (Must be available to work in CST Timezone)
Experience Required: 8+ Years
Department: Engineering
Employment Type: Full-Time

Role Overview
Ekshvaku Tech Innovations is looking for a highly experienced Senior DevOps Engineer who excels at building scalable, automated, and resilient infrastructure for modern distributed systems and AI-driven platforms.
You will lead DevOps strategy, modernize our infrastructure, and ensure high performance across multi-environment deployments. This role requires strong hands-on expertise, architectural thinking, excellent documentation discipline, and the ability to collaborate with cross-functional engineering teams.

Key Responsibilities

CI/CD & Automation

Design, implement, and maintain scalable CI/CD pipelines for Prerel, QA, and Production.
Continuously improve build, release, and deployment workflows.
Reduce manual ops through automation and scripting.

Infrastructure Engineering

Automate provisioning and configuration using Terraform, Ansible, and similar IaC tools.
Architect, deploy, and maintain cloud infrastructure (AWS/GCP).
Lead modernization initiatives: containerization, orchestration, microservices optimization.
Implement high availability setups, DR strategies, and cost-optimized infrastructure.

AI/ML Operations

Support AI/ML model training and deployment pipelines.
Manage GPU orchestration and scalable model-serving environments.
Work closely with AI and Data Engineering teams.

Monitoring, Security & Observability

Implement centralized monitoring, logging, and alerting systems.
Improve system reliability and incident response times.
Ensure cloud security best practices (IAM, network security, zero-trust).
Contribute to compliance efforts for HIPAA, SOC2, ISO 27001.

Documentation & Collaboration

Maintain clear documentation, runbooks, and architecture diagrams.
Collaborate with Engineering, Product, QA, and AI teams.
Evaluate new DevOps tools and AI-powered automation capabilities.

Required Skills & Experience

8+ years in DevOps or Cloud Infrastructure roles.
Strong expertise in GitHub Actions and CI/CD pipeline architecture.
Deep understanding of AWS or GCP (VPC, IAM, security, autoscaling).
Production-level experience with Docker & Kubernetes (EKS/GKE).
Proficiency in scripting: Python, Bash, or Go.
Strong experience with Terraform, Ansible, CloudFormation.
Solid understanding of monitoring tools (Prometheus, Grafana, CloudWatch, ELK).
Experience supporting AI/ML pipelines (MLflow, Kubeflow, SageMaker, Vertex AI).
Knowledge of security best practices & compliance frameworks.
Excellent communication and documentation skills.

Preferred Qualifications

Experience with GPU workloads and ML orchestration.
Knowledge of event-driven or serverless architectures (Lambda, Cloud Run).
Exposure to GitOps tools (Argo CD, Flux).
Background in AIOps (Dynatrace Davis, New Relic AI).
AWS DevOps Engineer Professional or equivalent certification.

Success Indicators

90%+ reduction in manual deployment tasks within 6 months.
Fully standardized infrastructure documentation across all environments.
Reduced downtime and faster incident resolution through observability.
Improved developer velocity and increased deployment frequency.
Adoption of AI-assisted automation and predictive monitoring.

Quick apply

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