remote cloud engineer with 1 year experience Jobs – 170 Open Jobs Today

DevOps / MLOps / Infra Engineer
SecHorizon Technologies Pvt. Ltd —Remote
- ₹13,00,000 - ₹15,00,000 a year
- Remote
- DevOps
- Engineering
- AWS
- Paid time off
- Work from home
Quick apply
9d
Junior DevOps Engineer
CSG —Remote
- Email
- Remote
- Azure
- Computer Science
- DevOps
- Work from home
9d
Support Engineer III
LivePerson —Remote
- Remote
- English
- Java
- SQL
- Health insurance
- Paid time off
Salesforce Data Cloud Architect
FuGenEd ( Creating Opportunities For Deserving) —Remote
- ₹1,00,000 - ₹1,15,000 a month
- Full-time
- Remote
- Azure
- Computer Science
- Salesforce
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8h
Senior Data Cloud Engineer
QUANTUMPOD TECHNOLOGIES —Remote
- ₹1,00,000 - ₹1,20,000 a month
- Full-time
- Remote
- Azure
- Salesforce
- AWS
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Hiring for Fresher -B. tech (CS/IT)
Kerner Norland —Remote
- ₹2,00,000 - ₹8,00,000 a year
- Full-time
- Remote
- Computer Science
- Software troubleshooting
- Java
- Work from home
- Flexible schedule
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3d
Data Platform Engineer
ᴠᴡᴏʀᴋᴇʀ ꜱᴏʟᴜᴛɪᴏɴꜱ ɪɴᴅɪᴀ ᴘᴠᴛ. ʟᴛᴅ. —Remote
- From ₹30,000 a month
- Part-time
- Remote
- Azure
- DevOps
- SQL
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AI/Ml Engineer (Python, React)
optimhire —Remote
- ₹3,93,473.71 - ₹45,00,000.00 a year
- Full-time
- Remote
- Azure
- Master's degree
- AWS
Quick apply
1d
Azure AI Foundry Engineer
Data PlatformExperts —Remote
- ₹5,00,000 - ₹15,00,000 a year
- Full-time
- Remote
- Hospitality
- Azure
- Master's degree
- Work from home
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Director of Engineering
Rolling Arrays —Remote
- ₹3,00,000 - ₹5,00,000 a month
- Full-time
- Remote
- Azure
- Oracle
- Master's degree
Quick apply
3d
MERN Stack + Gen AI developer
Tier5 Technology Solutions Pvt Ltd —Remote
- ₹10,55,061.31 - ₹17,44,103.74 a year
- Full-time
- Remote
- Azure
- System design
- Master's degree
- Health insurance
- Work from home
Quick apply
Workspace one - Technical Consultant L3
Yo Hr Consultancy —Remote
- From ₹20,00,000 a year
- Full-time
- Remote
- Azure
- Windows
- Bachelor's degree
Quick apply
Staff Engineer - Platform Engineering
HighLevel —Remote
- Remote
- APIs
- AI
- SDLC
Staff Database Reliability Engineer ( PostgreSQL + Cloud)- Remote
Rackspace —Remote
- Full-time
- Remote
- Azure
- SQL
- AWS
- Work from home
9d
Senior AIOps Engineer I
Porch Group Media —Remote
- ₹24,75,000 - ₹34,65,000 a year
- Full-time
- Remote
- Computer Science
- Master's degree
- SQL
- Paid holidays
- Health insurance
Senior Data and Backend Engineer
VWorker Solutions India Pvt Ltd —Remote
- From ₹30,000 a month
- Part-time
- Remote
- Computer Science
- English
- Java
- Work from home
Quick apply
4d
Data / Gen AI Engineer
INDUANSH PRIVATE LIMITED —Remote
- ₹80,000 - ₹1,00,000 a month
- Full-time
- Remote
- DevOps
- AWS
- Machine learning
Quick apply
3d
Senior Applied AI & Integration Engineer (Healthcare Automation)
Talisman Solutions —Remote
- ₹12,00,000 - ₹20,00,000 a year
- Full-time
- Remote
- Azure
- Master's degree
- SQL
- Health insurance
- Paid time off
Quick apply
Software Engineer III Contractor
TechnologyAdvice —Remote
- ₹480 - ₹970 a day
- Full-time
- Remote
- Databases
- AWS
- JavaScript
Quick apply
2d
Neos-5 (TSE)
Gigmo Solutions —Remote
- Full-time
- Remote
- Software troubleshooting
- B.Sc
- Windows
- Work from home

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DevOps / MLOps / Infra Engineer

SecHorizon Technologies Pvt. Ltd
Remote

Quick apply

Job details

Contractual / Temporary | Freelance
₹13,00,000 - ₹15,00,000 a year
9 days ago

Benefits

Paid time off
Work from home
Paid sick time

Qualifications

CI/CD
Cloud infrastructure
DevOps
Engineering
Git
AWS
Bachelor's degree
Terraform
Continuous integration
GitHub
APIs
Python
Identity & access management

Full job description

Key responsibilities

· CI/CD pipeline. Design and own the full CI/CD pipeline from day one: linting, unit tests, integration tests, and critically, the ML evaluation harness as a first-class CI gate. Every PR that touches extraction code automatically runs against the full ground truth dataset. A drop in accuracy blocks the merge - automatically, not by asking someone to check.

· ML pipeline infrastructure. Own the infrastructure that the ML engineers' work runs on: model training jobs (GPU or cloud-based), experiment tracking (MLflow, Weights & Biases, or equivalent), model versioning and the model registry, and the active learning trigger pipeline that kicks off retraining when reviewer corrections hit a threshold.

· IaC and cloud infrastructure. All cloud resources defined as code (Terraform or Pulumi) from Week 1. No hand-crafted resources, no configuration drift. You own networking, storage, secrets management, environment parity between dev, staging, and production, and cloud cost governance - especially for training compute and cloud OCR, the two biggest variable cost lines.

· Production operations. Own the production deployment process: environment configuration, secrets rotation, database backups, alerting on queue depth, error rate and latency thresholds, and a runbook for every common failure mode. On-call starts with you.

· Security and load testing. Lead the security review: authentication flows, API key handling, file storage access controls, input sanitisation. Commission and coordinate the penetration test. Run load testing against the pipeline - simulate concurrent document uploads, identify the throughput ceiling, and fix the top bottlenecks before launch.

· Cost and compute governance. Model training and cloud OCR are variable costs that can surprise you. Own the cost model: what does each training run cost, what is the cost per document at current OCR provider pricing, and what does the margin look like at 10× volume? If GPU-accelerated OCR becomes the right call, you make that recommendation with numbers behind it.

key deliverables

CI/CD pipeline - linting, tests, ML evaluation harness as a merge gate
ML experiment tracking and model registry
IaC - all cloud resources, environments, secrets, cost governance
Active learning trigger infrastructure - corrections → retraining → evaluation → deploy
Production deployment pipeline and operator runbook
Security review and penetration test coordination
Load testing - throughput ceiling documented, top bottlenecks resolved
Cloud cost model - per-document cost at 1×, 10×, 100× volume

Technical skills and experience

Minimum 4+ years in a DevOps, MLOps, or platform engineering role - not one or the other, genuinely both
Bachelor’s degree in engineering or science
CI/CD pipeline ownership: GitHub Actions, CircleCI, or equivalent. You have built pipelines from scratch, not just maintained them
Infrastructure as code: Terraform or Pulumi. You write it, not just read it
ML infrastructure experience: you have supported a team running models in production - training pipelines, experiment tracking, model versioning, deployment
Cloud platform depth on AWS or GCP - networking, IAM, secrets, storage, compute cost management
Strong enough on Python to read, debug, and instrument the ML team's training code without their help

Nice to have

Experience with ML experiment tracking tools (MLflow, Weights & Biases, Neptune)
Familiarity with active learning or continual learning pipeline infrastructure
Experience with GPU compute management - spot instances, cost optimisation, training job scheduling
Background supporting document processing or OCR pipelines at scale
SOC 2 readiness or security compliance experience
Experience with ML model monitoring in production - drift detection, performance degradation alerts

Pay: ₹1,300,000.00 - ₹1,500,000.00 per year

Benefits:

Paid sick time
Paid time off
Work from home

Application Question(s):

What is your current CTC?
What is your Expected CTC ?
What is your Notice period time ?

Education:

Bachelor's (Preferred)

Experience:

DevOps: 4 years (Required)

Work Location: Remote

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