Lead DevOps Engineer, Top e commerce company only

Primary (Must Have):

 5-7 years of total experience in IT industry

 Necessary to have exposure to Azure Cloud

 5-7 years of experience Build/Release and DevOps combined for Saas/Web/Desktop applications

 5-7 years working with CI / CD like Jenkins or Bamboo or other tools

 Experience in Kubernetes, Swarm and Docker

 Experience in working with Linux (Centos/Ubuntu)

 Experience with configuration and management tools (i.e. Terraform, Ansible, Salt)

 Experience in architecting, maintaining, and streamlining our automated build and release pipeline from code compilation, automated testing, to deploying releases to multiple environments

 You should be able to drive adoption of CI/CD, deployment automation, and release engineering standard methodologies

 Detail-oriented and great problem solver

 Comfortable giving feedback to team members and handling personal situations

 Excellent at multi-tasking and able to handle competing priorities

 Problem solving, digging into issues and owning tasks to completion

 Strong team player who is open to give and receive feedback

 A passion for technology and thrives in a dynamic environment with a focus on creative innovation over absolute completion

 Communication and presentation skills

Key Responsibilities:            

 Responsible for the continuous and on-time delivery of application releases supporting mission-critical services

 Support and improve our tools for continuous integration (CI) and continuous delivery (CD). You can demonstrate measurable improvements in our delivery pipelines

 Responsible for navigating cloud solutions, on-premise solutions, and hybrid solutions

 Responsible for infrastructure as code (IaC) tooling

 Build automated release pipelines that package, test and deploy code

 Build out and maintain a suitable tracking dashboards and metrics

Position: DevOps Engineer / Senior DevOps Engineer

Experience: 3 to 6 Years

Key Skills: AWS, Terraform, Docker, Kubernetes, DevSecOps pipeline

Job Description:

• AWS Infrastructure: Architect, deploy, and manage AWS services like EC2, S3, RDS, Lambda, SageMaker, API Gateway, and VPC.
• Networking: Proficient in subnetting, endpoints, NACL, security groups, VPC flow logs, and routing.
• API Management: Design and manage secure, scalable APIs using AWS API Gateway.
• CI/CD Pipelines: Build and maintain CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy.
• Automation & IaC: Use Terraform and CloudFormation for automating infrastructure management.
• Containerization & Kubernetes: Expertise in Docker, Kubernetes, and managing containerized deployments.
• Monitoring & Logging: Implement monitoring with AWS CloudWatch, CloudTrail, and other tools.
• Security: Apply AWS security best practices using IAM, KMS, Secrets Manager, and GuardDuty.
• Cost Management: Monitor and optimize AWS usage and costs.
• Collaboration: Partner with development, QA, and operations teams to enhance productivity and system reliability.

Location: Bangalore

Experience: 2–5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most systems don’t fail because of one big outage.

They fail because reliability is treated as an afterthought.

Right now, uptime depends too much on individual heroics.

That doesn’t scale.

This role exists to build a reliability system where:

• Uptime is predictable
• Failures are contained
• Escalations don’t depend on leadership

What you’ll do

You will not just monitor systems.

You will own reliability as a product.

1. Drive uptime to production-grade reliability

• Improve system uptime to 99.9% customer-facing SLA within 4 months
• Define and track:
• SLAs / SLOs / error budgets
• Ensure reliability is measured from the customer’s perspective, not internal metrics

2. Build incident response as a system

• Set up a 24/7 incident response rotation across 3 engineers
• Eliminate dependency on leadership (no single escalation point)
• Define:
• Incident severity levels
• Response playbooks
• Escalation protocols
• Ensure fast detection → containment → resolution

3. Contain and fix erratic system behavior

• Identify and resolve:
• Latency spikes
• Downtime incidents
• Integration failures
• Build guardrails to prevent recurrence
• Focus on root cause elimination, not temporary fixes

4. Create continuous reliability feedback loops

• Work closely with engineering teams to:
• Surface recurring failure patterns
• Improve build quality
• Reduce production bugs
• Ensure learnings from incidents directly improve future releases

5. Improve observability and monitoring

• Build dashboards and alerts for:
• System health
• Performance metrics
• Failure signals
• Ensure issues are detected before customers report them

6. Reduce operational fragility

• Remove single points of failure (people, systems, workflows)
• Improve system resilience across:
• Deployments
• Integrations
• Runtime environments

What success looks like

• Uptime reaches 99.9%+ reliably
• Incidents are:
• Detected early
• Contained quickly
• Resolved permanently
• No dependency on a single individual for escalation
• System behavior becomes predictable and stable
• Engineering teams ship with higher reliability confidence

Who you are

• You have 2-5 years of experience in SRE / DevOps / backend systems
• You have worked on production systems with real uptime expectations
• You think in:
• Systems
• Failure modes
• Trade-offs
• You are comfortable debugging live, high-pressure environments

What will make you stand out

• Experience with:
• Distributed systems
• Cloud infrastructure (AWS / Azure / GCP)
• Monitoring & alerting tools
• Have built or improved:
• Incident response systems
• Reliability frameworks
• Strong debugging skills across:
• Infra
• Application
• Integrations

Compensation

₹60,000/month (fixed)

(Aligned with role scope and impact expectations)

Why join

• You will define reliability standards for a production AI platform
• Your work directly impacts:
• Customer trust
• Product performance
• Enterprise readiness
• You will move the system from reactive → predictable

What this role is not

• Not just monitoring dashboards
• Not limited to handling tickets
• Not dependent on escalation to leadership

What this role is

• A builder of reliability systems
• A guardian of uptime and performance
• A multiplier of engineering quality

One question to self-evaluate

Can you build a system where downtime is rare, predictable, and never dependent on a single person?

Job Requirements 

Required Experience 

5–6 years of hands-on experience in Salesforce DevOps, release engineering, or deployment 

management. 

Strong expertise in Salesforce deployment processes, including CI/CD pipelines. 

Significant hands-on experience with at least two of the following tools: Gearset, Copado, 

Flosum. 

Solid understanding of Salesforce architecture, metadata, and development lifecycle. 

Familiarity with version control systems (e.g., Git) and agile methodologies. 

Key Responsibilities 

Design, implement, and manage CI/CD pipelines for Salesforce deployments using Gearset, 

Copado, or Flosum. 

Automate and optimize deployment processes to ensure efficient, reliable, and repeatable 

releases across Salesforce environments. 

Collaborate with development, QA, and operations teams to gather requirements and ensure 

alignment of deployment strategies. 

Monitor, troubleshoot, and resolve deployment and release issues. 

Maintain documentation for deployment processes and provide training on best practices. 

Stay updated on the latest Salesforce DevOps tools, features, and best practices. 

Technical Skills 

Skill Area Requirements 

Deployment ToolsHands-on with Gearset, Copado, Flosum for Salesforce deployments 

CI/CDBuilding and maintaining pipelines, automation, and release management 

Version ControlProficiency with Git and related workflows 

Salesforce PlatformUnderstanding of metadata, SFDX, and environment management 

Scripting 

Familiarity with scripting (e.g., Shell, Python) for automation (preferred) 

Communication 

Strong written and verbal communication skills 

Preferred Qualifications 

Bachelor’s degree in Computer Science, Information Technology, or related field. 

Certifications 

Salesforce certifications (e.g., Salesforce Administrator, Platform Developer I/II) are a plus. 

Experience with additional DevOps tools (Jenkins, GitLab, Azure DevOps) is beneficial. 

Experience with Salesforce DX and deployment strategies for large-scale orgs. 

Role: Full-Time, Long-Term Required: Docker, GCP, CI/CD Preferred: Experience with ML pipelines

OVERVIEW

We are seeking a DevOps engineer to join as a core member of our technical team. This is a long-term position for someone who wants to own infrastructure and deployment for a production machine learning system. You will ensure our prediction pipeline runs reliably, deploys smoothly, and scales as needed.

The ideal candidate thinks about failure modes obsessively, automates everything possible, and builds systems that run without constant attention.

CORE TECHNICAL REQUIREMENTS

Docker (Required): Deep experience with containerization. Efficient Dockerfiles, layer caching, multi-stage builds, debugging container issues. Experience with Docker Compose for local development.

Google Cloud Platform (Required): Strong GCP experience: Cloud Run for serverless containers, Compute Engine for VMs, Artifact Registry for images, Cloud Storage, IAM. You can navigate the console but prefer scripting everything.

CI/CD (Required): Build and maintain deployment pipelines. GitHub Actions required. You automate testing, building, pushing, and deploying. You understand the difference between continuous integration and continuous deployment.

Linux Administration (Required): Comfortable on the command line. SSH, diagnose problems, manage services, read logs, fix things. Bash scripting is second nature.

PostgreSQL (Required): Database administration basics—backups, monitoring, connection management, basic performance tuning. Not a DBA, but comfortable keeping a production database healthy.

Infrastructure as Code (Preferred): Terraform, Pulumi, or similar. Infrastructure should be versioned, reviewed, and reproducible—not clicked together in a console.

WHAT YOU WILL OWN

Deployment Pipeline: Maintaining and improving deployment scripts and CI/CD workflows. Code moves from commit to production reliably with appropriate testing gates.

Cloud Run Services: Managing deployments for model fitting, data cleansing, and signal discovery services. Monitor health, optimize cold starts, handle scaling.

VM Infrastructure: PostgreSQL and Streamlit on GCP VMs. Instance management, updates, backups, security.

Container Registry: Managing images in GitHub Container Registry and Google Artifact Registry. Cleanup policies, versioning, access control.

Monitoring and Alerting: Building observability. Logging, metrics, health checks, alerting. Know when things break before users tell us.

Environment Management: Configuration across local and production. Secrets management. Environment parity where it matters.

WHAT SUCCESS LOOKS LIKE

Deployments are boring—no drama, no surprises. Systems recover automatically from transient failures. Engineers deploy with confidence. Infrastructure changes are versioned and reproducible. Costs are reasonable and resources scale appropriately.

ENGINEERING STANDARDS

Automation First: If you do something twice, automate it. Manual processes are bugs waiting to happen.

Documentation: Runbooks, architecture diagrams, deployment guides. The next person can understand and operate the system.

Security Mindset: Secrets never in code. Least-privilege access. You think about attack surfaces.

Reliability Focus: Design for failure. Backups are tested. Recovery procedures exist and work.

CURRENT ENVIRONMENT

GCP (Cloud Run, Compute Engine, Artifact Registry, Cloud Storage), Docker, Docker Compose, GitHub Actions, PostgreSQL 16, Bash deployment scripts with Python wrapper.

WHAT WE ARE LOOKING FOR

Ownership Mentality: You see a problem, you fix it. You do not wait for assignment.

Calm Under Pressure: When production breaks, you diagnose methodically.

Communication: You explain infrastructure decisions to non-infrastructure people. You document what you build.

Long-Term Thinking: You build systems maintained for years, not quick fixes creating tech debt.

EDUCATION

University degree in Computer Science, Engineering, or related field preferred. Equivalent demonstrated expertise also considered.

TO APPLY

Include: (1) CV/resume, (2) Brief description of infrastructure you built or maintained, (3) Links to relevant work if available, (4) Availability and timezone.