DYT - Do Your Thing, is an app, where all social media users can share brands they love with their followers and earn money while doing so! We believe everyone is an influencer. Our aim is to democratise social media and allow people to be rewarded for the content they post. How does DYT help you? It accelerates your career through collaboration opportunities with top brands and gives you access to a community full of experts in the influencer space.

Role: DevOps

Job Description:

We are looking for experienced DevOps Engineers to join our Engineering team. The candidate will be working with our engineers and interact with the tech team for high quality web applications for a product.

Required Experience

Devops Engineer with 2+ years of experience in development and production operations supporting for Linux & Windows based applications and Cloud deployments (AWS/GC stack)
Experience working with Continuous Integration and Continuous Deployment Pipeline
Exposure to managing LAMP stack-based applications
Experience Resource provisioning automation using tools such as CloudFormation, terraform and ARM Templates.
Experience in working closely with clients, understanding their requirements, design and implement quality solutions to meet their needs.
Ability to take ownership on the carried-out work
Experience coordinating with rest of the team to deliver well-architected and high-quality solutions.
Experience deploying Docker based applications
Experience with AWS services.
Excellent verbal and written communication skills

Desired Experience

Exposure to AWS, google cloud and Azure Cloud
Experience in Jenkins, Ansible, Terraform
Build Monitoring tools and respond to alarms triggered in production environment
Willingness to quickly become a member of the team and to do what it takes to get the job done
Ability to work well in a fast-paced environment and listen and learn from stakeholders
Demonstrate a strong work ethic and incorporate company values in your everyday work.

Do Your Thng

https://doyourthng.com/

Role: DevOps

Job Description:

Required Experience

Devops Engineer with 2+ years of experience in development and production operations supporting for Linux & Windows based applications and Cloud deployments (AWS/GC stack)
Experience working with Continuous Integration and Continuous Deployment Pipeline
Exposure to managing LAMP stack-based applications
Experience Resource provisioning automation using tools such as CloudFormation, terraform and ARM Templates.
Experience in working closely with clients, understanding their requirements, design and implement quality solutions to meet their needs.
Ability to take ownership on the carried-out work
Experience coordinating with rest of the team to deliver well-architected and high-quality solutions.
Experience deploying Docker based applications
Experience with AWS services.
Excellent verbal and written communication skills

Desired Experience

Exposure to AWS, google cloud and Azure Cloud
Experience in Jenkins, Ansible, Terraform
Build Monitoring tools and respond to alarms triggered in production environment
Willingness to quickly become a member of the team and to do what it takes to get the job done
Ability to work well in a fast-paced environment and listen and learn from stakeholders
Demonstrate a strong work ethic and incorporate company values in your everyday work.

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

Shubham Vishwakarma

Full Stack Developer - Averlon

I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.

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About CEDRETO MARKETING PRIVATE LIMITED

Founded :

2019

Type :

Product

Size :

20-100

Stage :

Raised funding

About

Do Your Thng helps people support what they love and in turn get rewarded while doing so. We are the world’s first shared economy platform for digital profiles

Ankit Agarwal

Connect

Company social profiles

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Senior DevOps Engineer

AWS Infrastructure, Kubernetes & Production Operations

📍 Mumbai (On-site) | Full-time | 5-8 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior DevOps Engineer for a dedicated client engagement building an AI platform that turns a written prompt into a working, deployed web application.

The platform runs entirely on our own Kubernetes on AWS in Mumbai: multiple clusters, self-hosted Kafka, PostgreSQL, Redis and a complete observability stack, with untrusted AI-generated code executing inside isolated sandboxes.

This role owns that infrastructure end to end.

The mandatory requirement is hands-on production ownership of self-managed Kubernetes and self-hosted stateful services.

This is not a managed-services role: almost nothing here is a click in a console, and there is no platform team above you to escalate to.

You will pair daily with the Tech Architect who owns the architecture, and you own its execution, which covers clusters, networking, pipelines, observability, isolation, and the monthly cloud bill as a number you can explain line by line.

The role carries a deliberate path into LLM infrastructure over the first year, and we are saying so up front because we want someone who wants that direction.

A typical week includes rehearsing a cluster upgrade in a lower environment, tracing an unexplained cost line to its source, tuning autoscaling so non-production shuts down cleanly overnight, and hardening the boundary that keeps AI-generated code away from everything else.

Responsibilities:

Kubernetes Platform Ownership

Own multiple EKS clusters separated by trust boundary, covering platform services, messaging, sandboxed build environments, and hosting for customers' published applications.

Node pools, taints and tolerations, resource requests versus limits, pod disruption budgets, and the scheduling decisions that determine whether a cluster is efficient or merely large.

Plan and execute version upgrades without customer-visible downtime.

Infrastructure as Code

Everything in Terraform, with no console changes that are not in code.

Build reusable cluster modules rather than copies.

Keep state and reality in sync, and treat drift as a defect.

CI/CD and Release Engineering

Own the GitHub Actions pipelines and self-hosted runners: OIDC-based AWS authentication with no stored credentials, image builds, and rollouts across development, QA and UAT.

Move manifest templating from scripted substitution to Kustomize or Helm.

Make a rollback as routine as a deploy.

Network Architecture and Workload Isolation

VPC and subnet design, ingress through load balancers and service mesh, and eBPF-based network policy.

The platform runs code we did not write, so the isolation boundary is a primary deliverable: default-deny egress, kernel-level sandboxing for untrusted workloads, no platform credentials inside tenant pods, and one project per pod with nothing shared between customers.

Observability

Operate a self-hosted stack of Prometheus, Grafana, Loki, Tempo, OpenTelemetry and Alertmanager.

Build alerts engineers trust rather than mute.

Know what belongs in a metric, a log and a trace, and keep metric cardinality under control as tenant count grows.

Autoscaling and Capacity

Node autoscaling with Karpenter, and workload autoscaling on the signal that actually matters, which is queue depth or a schedule rather than CPU wherever CPU misleads.

Implement scale-to-zero across non-production and warm-pool patterns where cold starts are unacceptable.

Size capacity from measurement, not assumption.

Cost Operations

Own the cloud bill as an engineering artifact.

Attribute spend per cluster and per environment, right-size against real utilisation, apply Spot where interruption is safe and never where it is not, and investigate any line you cannot explain.

Instance family selection is a cost decision: match the family to the resource that binds first.

Self-Hosted Stateful Services

Operate Kafka, PostgreSQL and Redis in-cluster, covering persistence, replication, failover, upgrades, and backups you have actually restored from.

Rehearse the restore and record how long it took.

Security Posture

IAM and workload identity, migration away from static credentials, secret management, web application firewall rules, and closing the gaps you find.

Credential rotation and least privilege are ongoing work, not a one-time project.

AI Infrastructure (Growth Path)

Within the first year, take on the infrastructure serving AI workloads: multi-provider LLM gateway and routing, token and cost attribution, rate-limit-aware capacity planning, LLM tracing with Langfuse, vector databases such as pgvector and Qdrant, and self-hosted embedding models including GPU nodes on Kubernetes.

This is inference operations, not model training.

AI-Assisted Engineering Discipline

Use Claude, Cursor, and similar tools day to day for infrastructure code, scripts, and pipelines.

Set the team standard for safe use, review, and validation of AI-generated infrastructure before it ships.

Requirements:

Hands-on Production Ownership of Self-Managed Kubernetes (Mandatory)

Must have personally operated a production Kubernetes estate with operational responsibility including oncall, incident response, and upgrade ownership.

You understand why a cluster ends up over-provisioned and how to fix it.

Managed-platform-only experience, or Kubernetes limited to deploying into a cluster someone else runs, does not qualify.

5+ Years of Overall Engineering Experience

With at least 3 years in DevOps, SRE, or platform engineering.

This is not an ML science role.

A strong infrastructure background with genuine appetite for AI infrastructure is exactly the profile we want.

Proven Ability to Learn an Unfamiliar Technology to Production Depth

We are hiring for trajectory as much as for current tooling, so this carries real weight.

Be ready to walk us through something you picked up from scratch and then owned in production: what you got wrong in the first attempt, how you found out, and what you would do differently.

A candidate with a shorter tool list and a strong record of learning will be preferred over a longer list learned only at the surface.

Self-Hosted Stateful Services in Production

One or more of Kafka, PostgreSQL, Redis, or Elasticsearch operated by you, not by a managed service.

Backups, restores, failover, and version upgrades.

This matters more than any other line here, because almost everything we run, we run ourselves.

Infrastructure as Code Depth

Terraform in anger, including modules, state management, and recovering from drift.

Docker and image optimisation.

AWS at Depth

EKS, EC2, VPC, IAM, S3, RDS or Aurora, and load balancing.

You can read a bill and explain where the money went.

Networking Depth

VPC and subnet design, NAT and egress paths, load balancer behaviour, DNS, TLS, and Kubernetes networking.

Able to reason about address exhaustion and routing limits before they are hit rather than after.

Observability Depth

Production experience with Prometheus, Grafana, and a log or trace backend.

Comfortable instrumenting with OpenTelemetry.

Able to define alerts that are actionable and to resist the cardinality that makes a metrics system collapse.

CI/CD and Deployment Automation

Strong with GitHub Actions or GitLab CI, including self-hosted runners and short-lived cloud credentials.

Experience making deploys and rollbacks safe and routine.

Cost Management for Cloud Infrastructure

Demonstrated ownership of a cloud bill: right-sizing, reserved capacity or savings plans, Spot strategy, and tracing an anomaly to its cause.

Reliability Engineering

Comfortable defining SLOs and being accountable for them.

Experience with graceful shutdown, connection draining, disruption budgets, and long-lived connections such as WebSockets that autoscaling can otherwise sever.

Nice to Have:

arm64 and Graviton estates
eBPF networking with Cilium
container runtime isolation such as gVisor, Kata, or Firecracker
Kafka operations including partition and consumer-group management
service mesh with Istio
GPU workloads on Kubernetes
hands-on familiarity with LLM observability tooling such as Langfuse or LangSmith
India data-protection and residency requirements under the DPDP Act
AWS certifications

Senior DevOps Engineer

AWS Infrastructure, Kubernetes & Production Operations

📍 Mumbai (On-site) | Full-time | 5-8 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior DevOps Engineer for a dedicated client engagement building an AI platform that turns a written prompt into a working, deployed web application.

This role owns that infrastructure end to end.

The mandatory requirement is hands-on production ownership of self-managed Kubernetes and self-hosted stateful services.

This is not a managed-services role: almost nothing here is a click in a console, and there is no platform team above you to escalate to.

The role carries a deliberate path into LLM infrastructure over the first year, and we are saying so up front because we want someone who wants that direction.

Responsibilities:

Kubernetes Platform Ownership

Own multiple EKS clusters separated by trust boundary, covering platform services, messaging, sandboxed build environments, and hosting for customers' published applications.

Node pools, taints and tolerations, resource requests versus limits, pod disruption budgets, and the scheduling decisions that determine whether a cluster is efficient or merely large.

Plan and execute version upgrades without customer-visible downtime.

Infrastructure as Code

Everything in Terraform, with no console changes that are not in code.

Build reusable cluster modules rather than copies.

Keep state and reality in sync, and treat drift as a defect.

CI/CD and Release Engineering

Own the GitHub Actions pipelines and self-hosted runners: OIDC-based AWS authentication with no stored credentials, image builds, and rollouts across development, QA and UAT.

Move manifest templating from scripted substitution to Kustomize or Helm.

Make a rollback as routine as a deploy.

Network Architecture and Workload Isolation

VPC and subnet design, ingress through load balancers and service mesh, and eBPF-based network policy.

Observability

Operate a self-hosted stack of Prometheus, Grafana, Loki, Tempo, OpenTelemetry and Alertmanager.

Build alerts engineers trust rather than mute.

Know what belongs in a metric, a log and a trace, and keep metric cardinality under control as tenant count grows.

Autoscaling and Capacity

Node autoscaling with Karpenter, and workload autoscaling on the signal that actually matters, which is queue depth or a schedule rather than CPU wherever CPU misleads.

Implement scale-to-zero across non-production and warm-pool patterns where cold starts are unacceptable.

Size capacity from measurement, not assumption.

Cost Operations

Own the cloud bill as an engineering artifact.

Attribute spend per cluster and per environment, right-size against real utilisation, apply Spot where interruption is safe and never where it is not, and investigate any line you cannot explain.

Instance family selection is a cost decision: match the family to the resource that binds first.

Self-Hosted Stateful Services

Operate Kafka, PostgreSQL and Redis in-cluster, covering persistence, replication, failover, upgrades, and backups you have actually restored from.

Rehearse the restore and record how long it took.

Security Posture

IAM and workload identity, migration away from static credentials, secret management, web application firewall rules, and closing the gaps you find.

Credential rotation and least privilege are ongoing work, not a one-time project.

AI Infrastructure (Growth Path)

This is inference operations, not model training.

AI-Assisted Engineering Discipline

Use Claude, Cursor, and similar tools day to day for infrastructure code, scripts, and pipelines.

Set the team standard for safe use, review, and validation of AI-generated infrastructure before it ships.

Requirements:

Hands-on Production Ownership of Self-Managed Kubernetes (Mandatory)

Must have personally operated a production Kubernetes estate with operational responsibility including oncall, incident response, and upgrade ownership.

You understand why a cluster ends up over-provisioned and how to fix it.

Managed-platform-only experience, or Kubernetes limited to deploying into a cluster someone else runs, does not qualify.

5+ Years of Overall Engineering Experience

With at least 3 years in DevOps, SRE, or platform engineering.

This is not an ML science role.

A strong infrastructure background with genuine appetite for AI infrastructure is exactly the profile we want.

Proven Ability to Learn an Unfamiliar Technology to Production Depth

We are hiring for trajectory as much as for current tooling, so this carries real weight.

Be ready to walk us through something you picked up from scratch and then owned in production: what you got wrong in the first attempt, how you found out, and what you would do differently.

A candidate with a shorter tool list and a strong record of learning will be preferred over a longer list learned only at the surface.

Self-Hosted Stateful Services in Production

One or more of Kafka, PostgreSQL, Redis, or Elasticsearch operated by you, not by a managed service.

Backups, restores, failover, and version upgrades.

This matters more than any other line here, because almost everything we run, we run ourselves.

Infrastructure as Code Depth

Terraform in anger, including modules, state management, and recovering from drift.

Docker and image optimisation.

AWS at Depth

EKS, EC2, VPC, IAM, S3, RDS or Aurora, and load balancing.

You can read a bill and explain where the money went.

Networking Depth

VPC and subnet design, NAT and egress paths, load balancer behaviour, DNS, TLS, and Kubernetes networking.

Able to reason about address exhaustion and routing limits before they are hit rather than after.

Observability Depth

Production experience with Prometheus, Grafana, and a log or trace backend.

Comfortable instrumenting with OpenTelemetry.

Able to define alerts that are actionable and to resist the cardinality that makes a metrics system collapse.

CI/CD and Deployment Automation

Strong with GitHub Actions or GitLab CI, including self-hosted runners and short-lived cloud credentials.

Experience making deploys and rollbacks safe and routine.

Cost Management for Cloud Infrastructure

Demonstrated ownership of a cloud bill: right-sizing, reserved capacity or savings plans, Spot strategy, and tracing an anomaly to its cause.

Reliability Engineering

Comfortable defining SLOs and being accountable for them.

Experience with graceful shutdown, connection draining, disruption budgets, and long-lived connections such as WebSockets that autoscaling can otherwise sever.

Nice to Have:

arm64 and Graviton estates
eBPF networking with Cilium
container runtime isolation such as gVisor, Kata, or Firecracker
Kafka operations including partition and consumer-group management
service mesh with Istio
GPU workloads on Kubernetes
hands-on familiarity with LLM observability tooling such as Langfuse or LangSmith
India data-protection and residency requirements under the DPDP Act
AWS certifications

Azure AI & DevOps Architect

at VDart Digital

2 recruiters

Posted by Martin Raj

Bengaluru (Bangalore)

13 - 20 yrs

₹40L - ₹60L / yr

DevOps

Azure OpenAI

Terraform

IAC

Platform as a Service (PaaS)

We are seeking a highly experienced Azure AI, AIOps & MLOps Architect to lead enterprise-scale AI platform engineering, cloud modernization, DevSecOps transformation, and intelligent automation initiatives.

The ideal candidate should possess deep expertise in Microsoft Azure, Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Kubernetes, Terraform, Azure DevOps, and enterprise observability platforms. The role will focus on designing scalable AI platforms, implementing MLOps and AIOps capabilities, enabling Agentic AI architectures, and driving cloud-native engineering practices across the organization.

Key Responsibilities

Cloud Architecture & Engineering

• Design and implement scalable, secure, and highly available solutions on Microsoft Azure.

• Define cloud architecture standards, reference architectures, and best practices.

• Lead cloud migration and modernisation initiatives across enterprise workloads.

• Implement multi-region disaster recovery and business continuity strategies.

• Oversee Azure networking, identity, security, and governance frameworks.

DevOps & CI/CD

• Architect and implement end-to-end CI/CD pipelines using Azure DevOps or GitHub Actions.

• Drive DevSecOps culture — embedding security scanning, quality gates, and compliance into the delivery pipeline.

• Champion Infrastructure-as-Code (IaC) practices using Terraform, Bicep, or ARM templates.

• Establish branching strategies, release management, and environment promotion standards.

• Define and enforce platform engineering standards and internal developer tooling.

AI & Machine Learning Integration

• Architect AI/ML solutions leveraging Azure AI services — Azure OpenAI, Azure Machine Learning, Azure AI Foundry, and Cognitive Services.

• Design intelligent automation and agentic workflows integrated into enterprise DevOps processes.

• Implement AI-powered capabilities such as code review assistance, anomaly detection, predictive analytics, and natural language automation.

• Define AI governance frameworks: model evaluation, prompt management, responsible AI, and cost controls.

• Design and implement enterprise MLOps frameworks.

• Build automated model training, validation, deployment, and monitoring pipelines.

• Establish model governance and lifecycle management.

Generative AI & Agentic AI

Design enterprise GenAI solutions using Azure OpenAI.
Build AI Agents using Azure AI Foundry.
Develop Agent-to-Agent communication patterns.
Implement Retrieval Augmented Generation (RAG) architectures.
Build enterprise Knowledge Management and AI Skill Registry platforms.
Design multi-agent orchestration frameworks.

Leadership & Stakeholder Engagement

• Serve as the technical authority and subject matter expert for Azure AI and DevOps practices.

• Mentor and guide junior architects, developers, and DevOps engineers.

• Collaborate with business stakeholders, product owners, and vendors to translate requirements into technical solutions.

• Produce architecture documentation, decision records (ADRs), and roadmaps.

• Represent the technology function in enterprise architecture forums and governance boards.

Required Qualifications

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

• 10+ years of experience in cloud engineering and architecture.

• 5+ years of hands-on experience with Microsoft Azure across compute, networking, storage, identity, and data services.

• Proven experience designing and implementing enterprise-grade CI/CD pipelines.

• Strong hands-on expertise with Infrastructure-as-Code (Terraform, Bicep, or ARM).

• Demonstrated experience architecting and deploying AI/ML solutions on Azure (Azure OpenAI, Azure ML, AI Foundry).

• Deep knowledge of DevSecOps principles, tools, and practices.

• Experience with containerisation and orchestration: Docker, Kubernetes (AKS).

• Proficiency in scripting and development: Python, PowerShell, Bash.

• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Microsoft Certified: Azure Solutions Architect Expert.

• Microsoft Certified: DevOps Engineer Expert.

• Microsoft Certified: Azure AI Engineer Associate.

• Experience with Azure API Management (APIM), Event Grid, and Azure Functions.

• Familiarity with Datadog, Prometheus, or equivalent observability platforms.

• Experience in the real estate, retail, or enterprise industry sector.

• Knowledge of agentic AI frameworks and LLM orchestration patterns (LangChain, Semantic Kernel, MCP).

• Background in building Internal Developer Platforms (IDP).

Technical Skills

Domain

Technologies / Tools

Cloud Platform

Microsoft Azure (UAE North / Global)

DevOps & CI/CD

Azure DevOps, GitHub Actions, Jenkins

IaC

Terraform, Bicep, ARM Templates

AI / ML

Azure OpenAI, Azure AI Foundry, Azure ML, Cognitive Services

Containers

Docker, Kubernetes (AKS), Azure Container Apps

Identity & Security

Microsoft Entra ID, Azure Policy, Defender for Cloud

Observability

Datadog, Azure Monitor, Log Analytics, Application Insights

Databases

Azure SQL, PostgreSQL, Cosmos DB

Languages

Python, PowerShell, Bash, YAML