Mid Level Cloud Dev Ops- India

Posted 2 Hours Ago
Be an Early Applicant
Hiring Remotely in IN
Remote
Mid level
Artificial Intelligence • Information Technology • Machine Learning • Consulting
The Role
Design, build, and operate AWS/Azure cloud infrastructure and DevOps workflows. Own IaC, CI/CD, container and serverless deployments, monitoring, security, cost optimization, and cloud migrations while automating operations and collaborating with development, data, and ML teams.
Summary Generated by Built In

About Cognify Analytics

At Cognify Analytics, we’re driven by a single belief: that data and AI should be accessible and transformative for every organization. Founded by a team of ex-Google and Meta technologists, we specialize in delivering cutting-edge analytics and cloud solutions to companies around the globe. Whether it’s deploying machine learning models, optimizing cloud infrastructure, or guiding enterprise migrations—our mission is to turn complex data challenges into real-world impact.

The Role

We’re seeking a Mid-Level Cloud/DevOps Engineer to independently design, build, and operate cloud infrastructure and DevOps workflows on AWS and Azure. In this role, you will own key infrastructure components end to end, drive CI/CD maturity, manage containerized and serverless workloads, and collaborate with development and data teams to ensure reliable, secure, and cost-optimized cloud operations. You will also support cloud migrations, build automation frameworks, and integrate AI-powered tools into operational workflows.

What You’ll Do

  • Design, provision, and manage production cloud infrastructure on AWS and Azure using Terraform, CloudFormation, CDK, Bicep, or Pulumi.

  • Build and maintain robust CI/CD pipelines for application and infrastructure deployments using GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline, or Azure DevOps.

  • Deploy and manage containerized workloads on Amazon ECS, EKS, or Azure Kubernetes Service (AKS), including service mesh and auto-scaling configurations.

  • Implement serverless architectures using AWS Lambda, Step Functions, API Gateway, EventBridge, Azure Functions, and Logic Apps.

  • Support and execute cloud migration projects, ensuring scalability, security, and minimal downtime during transitions.

  • Develop and maintain Django-based and Python back-end applications integrated with cloud-native services such as RDS, DynamoDB, ElastiCache, S3, and SQS.

  • Implement infrastructure monitoring, alerting, and observability using CloudWatch, Azure Monitor, Prometheus, Grafana, Datadog, or OpenTelemetry.

  • Automate operational workflows using Python, Bash, Go, or PowerShell and build self-healing infrastructure patterns.

  • Enforce security best practices including IAM, VPC design, WAF, secrets management (Secrets Manager, Vault), encryption, and compliance controls.

  • Optimize cloud costs through reserved instances, savings plans, right-sizing, and FinOps practices.

  • Collaborate with development, data engineering, and ML teams to support application deployments, data pipelines, and AI/ML workloads.

What We’re Looking For

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

  • 3–5 years of professional experience in cloud engineering, DevOps, or site reliability engineering with production responsibility.

  • Strong hands-on experience with AWS services (EC2, S3, RDS, Lambda, VPC, ECS/EKS, CloudFormation, IAM) and/or Azure services (VMs, AKS, App Service, Functions, ARM/Bicep).

  • Proficiency with infrastructure-as-code tools: Terraform (strongly preferred), CloudFormation, CDK, or Pulumi.

  • Experience building and managing CI/CD pipelines with GitHub Actions, GitLab CI, Jenkins, or cloud-native pipeline services.

  • Working knowledge of Docker, Kubernetes, and container orchestration in production environments.

  • Strong scripting skills in Python, Bash, or Go for automation and tooling.

  • Solid understanding of networking (VPC, subnets, load balancers, DNS, CDN), security (IAM, RBAC, encryption), and compliance in cloud environments.

  • Experience with monitoring and observability tools: CloudWatch, Prometheus, Grafana, Datadog, or equivalent.

  • Familiarity with Agile methodologies and collaborative development workflows.

Preferred Qualifications

  • AWS Solutions Architect Associate/Professional, AWS DevOps Engineer Professional, or AZ-104/AZ-400 certification.

  • Experience with Django or FastAPI application deployment on cloud infrastructure.

  • Familiarity with GitOps practices and tools (ArgoCD, Flux).

  • Experience with database management on cloud platforms: RDS, Aurora, DynamoDB, Azure SQL, Cosmos DB.

  • Exposure to AI/ML infrastructure: SageMaker, Azure ML, Bedrock, GPU instance management, or model serving pipelines.

  • Experience with event-driven architectures using Kafka, SQS/SNS, EventBridge, or Kinesis.

  • Knowledge of FinOps principles and cloud cost optimization strategies.

Why Join UsWhy Join Us?

  • Join a team of industry veterans from Google, Meta, and top-tier tech companies.

  • Work on impactful, high-scale projects with leading global clients.

  • Enjoy a flexible, remote-first culture focused on innovation and excellence.

  • Competitive salary, equity options, and continuous learning opportunities.

  • Shape the future of cloud and AI infrastructure at a rapidly growing company.

Perks And Benefits Of Working With Us

  • Internet allowance

  • Laptop

  • PF

  • Paid PTO

  • Annual Bonus

  • Graduity

  • Yearly Team building experiences

  • Mentorship and sponsorship opportunities

  • Manager resources and support

  • Life & accidental insurance for additional protection.

We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other protected characteristic.

Skills Required

  • Bachelor's or Master's degree in Computer Science, Information Technology, or related field
  • 3-5 years professional experience in cloud engineering, DevOps, or site reliability engineering with production responsibility
  • Hands-on experience with AWS services (EC2, S3, RDS, Lambda, VPC, ECS/EKS, CloudFormation, IAM)
  • Experience with Azure services (VMs, AKS, App Service, Functions, ARM/Bicep)
  • Proficiency with infrastructure-as-code tools (CloudFormation, CDK, Pulumi)
  • Proficiency with Terraform
  • Experience building and managing CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline, Azure DevOps)
  • Working knowledge of Docker, Kubernetes, and container orchestration in production (ECS, EKS, AKS)
  • Strong scripting skills in Python, Bash, or Go for automation and tooling
  • Solid understanding of networking (VPC, subnets, load balancers, DNS, CDN), security (IAM, RBAC, encryption), and compliance in cloud environments
  • Experience with monitoring and observability tools (CloudWatch, Azure Monitor, Prometheus, Grafana, Datadog, OpenTelemetry)
  • Experience deploying and managing serverless architectures (AWS Lambda, Step Functions, API Gateway, EventBridge, Azure Functions, Logic Apps)
  • Experience supporting and executing cloud migration projects
  • Experience with Django or FastAPI application deployment on cloud infrastructure
  • AWS/Azure certifications (AWS Solutions Architect, AWS DevOps Engineer, AZ-104, AZ-400)
  • Familiarity with GitOps practices and tools (ArgoCD, Flux)
  • Exposure to AI/ML infrastructure and model serving (SageMaker, Azure ML, Bedrock, GPU instance management)
  • Experience with event-driven architectures (Kafka, SQS/SNS, EventBridge, Kinesis)
  • Knowledge of FinOps principles and cloud cost optimization strategies
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The Company
0 Employees
Year Founded: 2025

What We Do

Cogniify is a Bay Area-based AI execution firm that designs, builds, and deploys custom AI systems for Fortune 500 and Global 2000 companies. The company helps enterprises move from AI pilots to industrialized impact and enterprise-scale production, utilizing deep expertise in AI, advanced analytics, data engineering, and domain consulting.

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