Devops Engineer

Role & Responsibilities

We are looking for a hands-on AWS Cloud Engineer to support day-to-day cloud operations, automation, and reliability of AWS environments. This role works closely with the Cloud Operations Lead, DevOps, Security, and Application teams to ensure stable, secure, and cost-effective cloud platforms.

Key Responsibilities-

• Operate and support AWS production environments across multiple accounts
• Manage infrastructure using Terraform and support CI/CD pipelines
• Support Amazon EKS clusters, upgrades, scaling, and troubleshooting
• Build and manage Docker images and push to Amazon ECR
• Monitor systems using CloudWatch and third-party tools; respond to incidents
• Support AWS networking (VPCs, NAT, Transit Gateway, VPN/DX)
• Assist with cost optimization, tagging, and governance standards
• Automate operational tasks using Python, Lambda, and Systems Manager

Ideal Candidate

• Strong hands-on AWS experience (EC2, VPC, IAM, S3, ALB, CloudWatch)
• Experience with Terraform and Git-based workflows
• Hands-on experience with Kubernetes / EKS
• Experience with CI/CD tools (GitHub Actions, Jenkins, etc.)
• Scripting experience in **Python or Bash**
• Understanding of monitoring, incident management, and cloud security basics

Nice to Have-

• AWS Associate-level certifications
• Experience with Karpenter, Prometheus, New Relic
• Exposure to FinOps and cost optimization practices

Amura’s Vision 

We believe that the most under-appreciated route to releasing untapped human potential is to build a healthier body, and through which a better brain. This allows us to do more of everything that is important to each one of us.

Billions of healthier brains, sitting in healthier bodies, can take up more complex problems that defy solutions today, including many existential threats, and solve them in just a few decades.

Billions of healthier brains will make the world richer beyond what we can imagine today. The surplus wealth, combined with better human capabilities, will lead us to a new renaissance, giving us a richer and more beautiful culture.

These healthier brains will be equipped with deeper intellect, be less acrimonious, more magnanimous, and have a kinder outlook on the world, resulting in a world that is better than any previous time.

We find this vision of the future exhilarating. Our hopes and dreams are to create this future as quickly as possible and ensure that it is widely distributed and optimized to maximize all forms of human excellence. 

Role Overview 

We are looking for a highly skilled Senior DevOps Engineer (AI-Native Infrastructure & Platform Engineering) with deep expertise in AWS cloud infrastructure, automation, AI infrastructure operations, and modern DevOps/SRE practices.

This role goes beyond traditional DevOps and requires a seasoned specialist capable of building and operating AI-ready infrastructure platforms that support high-throughput APIs, LLM/AI workloads, GPU-based compute, data-intensive systems, real-time inference pipelines, and scalable ML platforms.

You will be responsible for architecting, automating, securing, and optimizing highly scalable and cost-efficient cloud environments that enable high-velocity engineering and AI teams. This is an ideal position for someone who combines technical ownership, an automation-first mindset, and a passion for developer productivity and platform reliability. 

Key Responsibilities 

Cloud Infrastructure & Platform Engineering (AWS) 

• Architect, deploy, and manage highly scalable and secure infrastructure on AWS. Design cloud platforms supporting AI/ML workloads, data pipelines, real-time APIs, and high-concurrency backend systems.
• Hands-on expertise with key AWS services including EC2, ECS/EKS, Lambda, RDS, DynamoDB, S3, VPC, CloudFront, IAM, CloudWatch, and GPU-enabled instances.
• Build and maintain Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or AWS CDK.
• Design multi-AZ and multi-region architectures for high availability and disaster recovery (HA/DR).
• Build reusable platform templates and shared infrastructure modules. 

AI/ML Infrastructure & MLOps 

• Build and maintain infrastructure for LLM applications, AI inference workloads, model serving platforms, vector databases, and feature stores.
• Support GPU-based workloads and optimize compute/storage usage.
• Enable scalable deployment patterns for AI applications using Kubernetes/EKS. Collaborate with Data Science and ML Engineering teams on model deployment, training/tuning of models, CI/CD for ML systems, experiment environments, and reproducibility.
• Support orchestration and deployment of AI workflows and inference services while implementing observability and reliability for AI pipelines. 

CI/CD, Automation & Developer Productivity 

• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
• Automate deployments, environment provisioning, and release workflows.
• Build self-service developer platforms, preview environments, and reusable deployment workflows to improve developer productivity.
• Implement automated patching, scaling, backups, cleanup workflows, and drift detection. 

Containers, Kubernetes & Platform Reliability

• Manage Docker-based environments, containerized applications, and optimize workloads using Kubernetes (EKS) or ECS/Fargate.
• Manage autoscaling, cluster health, node pools, ingress, service mesh, and workload isolation.
• Optimize infrastructure for performance, resilience, and cost-efficiency.
• Implement progressive deployment strategies including blue/green, canary, and rolling deployments. 

Observability, Incident Response & SRE Practices

• Implement observability stacks using CloudWatch, Prometheus, Grafana, ELK, Datadog, OpenTelemetry, or New Relic.
• Build actionable dashboards and intelligent alerting systems while defining and tracking SLIs, SLOs, and SLAs.
• Lead incident response, root cause analysis, and blameless postmortems to reduce operational toil and improve MTTR.

FinOps, Cost Governance & Security

• Continuously monitor and optimize cloud costs (compute utilization, storage lifecycle, GPU usage, and data transfer) using AWS Cost Explorer, Budgets, Trusted Advisor, CloudHealth, or Kubecost.
• Implement AWS security best practices for IAM, VPCs, security groups, NACLs, encryption, and manage secrets using KMS, SSM Parameter Store, or Vault.
• Build secure CI/CD pipelines with automated security checks, least-privilege access, audit logging, and ensure compliance readiness for ISO 27001, SOC2, and GDPR.

Collaboration, Leadership & Platform Culture

• Work closely with engineering, AI/ML, QA, product, and operations teams to drive a DevOps, SRE, GitOps, and automation-first culture.
• Mentor junior DevOps and Platform Engineers while creating and maintaining detailed runbooks, architecture diagrams, and platform documentation.

Skills & Qualifications

Must-Have:

• 7+ years of experience in DevOps, SRE, Platform Engineering, or Cloud Infrastructure Engineering.
• Strong expertise in AWS cloud architecture, services, and deep understanding of Kubernetes (EKS), containers, and cloud-native systems.
• Strong Infrastructure-as-Code expertise using Terraform, CloudFormation, or CDK. Strong Linux administration, networking, DNS, routing, and load balancing knowledge. Strong scripting/programming experience in Python, Bash, or Go (preferred). Experience with CI/CD automation, GitOps workflows, and observability platforms supporting scalable production systems.

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Here are answers to some questions you may have

Where is your office?

Chennai (Velachery)

Work Model

Work from Office – because great stories are built in person!

Do you have an online presence?

https://amura.ai (we are @AmuraHealth on all social media)

Why this role exists

Our infrastructure footprint is growing faster than our headcount, and we believe most of that

gap should be closed by automation and AI agents — not by hiring more humans to do toil. We

need someone early in their career who treats manual work as a bug, ships scripts and agents

instead of tickets, and wants to grow into deeper ownership over the next two years.

You will not be the most senior person on the team. You will be the one who multiplies the team.

What you'll own

In your first 1 months

• Take ownership of one slice of our CI/CD pipeline and make it measurably

faster, more reliable, or cheaper. We expect a number on a dashboard to move.

• Build at least three internal automations that replace manual ops toil —

using AI agents (Claude Code, agentic CLIs, scripted LLM workflows) as your force

multiplier.

• Be the first responder for a defined set of alerts. Write the runbooks. Drive

the alert volume down.

• Support senior engineers on AI/ML infrastructure (GPU nodes, inference

services, model deployment) — observe, document, and gradually take on contained

changes under review.

By 3 months you should be

• The go-to person for at least two production systems.

• Shipping routine infrastructure changes without needing senior review.

• Treating "manual" as a code smell.

Required (we will reject without these)

• 0–3 years hands-on experience with one major cloud (AWS, GCP, or

Azure — one is fine, depth beats breadth).

• Fluent in Linux command line, bash, and at least one scripting language

(Python or Go preferred).

• Have shipped something to production that real users hit. A side project

counts; a graded coursework lab does not.

• Comfortable with Docker — you can explain what an image vs. a

container is and why it matters.

• Working knowledge of networking fundamentals: DNS, HTTP/HTTPS,

TLS, ports, basic subnets — enough to debug "it works on my machine."

• Git fluency: branches, merges, rebases, conflict resolution.

• CI/CD pipelines — you have authored or substantially modified pipelines

in GitHub Actions, GitLab CI, ArgoCD, Jenkins, or similar. Not just "I clicked Re-run."

• Kubernetes basics — kubectl for real work, can read pod logs,

understand deployments and services, can debug a CrashLoopBackOff without

panicking. You do not need to have run a cluster; you do need to have lived inside one.

• Active user of AI coding agents (Claude Code, Cursor, Copilot, agentic

CLIs, etc.). You should be able to walk us through specific tasks where they made you

faster, and specific tasks where they failed you and how you noticed. "I have tried it" is

not enough.

Bonus (real plus, not required)

• Infrastructure as Code: Terraform, Pulumi, or Ansible.

• Observability: Prometheus/Grafana, Datadog, OpenTelemetry, any APM.

• Have built or extended an LLM-based agent — a custom MCP server, a

scripted multi-step workflow, an internal tool that calls models in a loop. Anything beyond

chat-with-Claude.

• Exposure to GPU workloads, model serving (vLLM, Triton, TGI, etc.), or

ML pipelines.

What we don't care about

• Whether your degree is in CS — or whether you have a degree at all.

• Brand-name companies on your resume.

• Certifications. They are fine. They do not substitute for having shipped.

How we work

• We default to automation. If you do something manually twice, the third

time you script it or hand it to an agent.

• AI agents are part of the workflow, not a novelty. Expect interview

questions about exactly how you use them — and where you have caught them being

wrong.

• Small, reversible changes beat big-bang rollouts.

• Postmortems are blameless and written down.

• We push back on each other. If you only execute, you will be unhappy

here.

How to apply

Send:

• Your resume.

• A short note (≤200 words) describing one infra or automation problem you

solved, and how AI agents factored in — or did not, and why. We read these. Generic

notes get rejected.

Internal note — delete before posting externally

• Comp band, location policy, team name, and reporting line marked

[CONFIRM] need to be filled in before this goes external.

• The Required list is intentionally tight: CI/CD and Kubernetes basics

promoted from bonus. Expect this to filter ~80% of typical junior DevOps applicants. The

remaining pool will skew toward people who have actually shipped infra at a startup, not

bootcamp grads or pure cloud-cert holders.

• IaC, observability, agent-building, and GPU/ML serving stay as bonus.

Promoting any of these to required at 0–3 yrs collapses the pool to near-zero or forces

hiring senior people at junior comp. If you want IaC required, re-level this to mid (3–5

yrs) and raise the band.

• Screening implication: the resume screen should explicitly check for

CI/CD pipeline authorship and any K8s-touching production work. If neither is on the

resume, reject at screen. Do not waste interview slots.

• Pipeline watch: if fewer than ~15 qualified resumes after 2 weeks of

active sourcing, the first thing to relax is the AI-agent-fluency bar (move to bonus and

screen for it in interview instead). Do not relax the "shipped to production" requirement

— that is the load-bearing filter.

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.

Key Responsibilities: 

• Develop and Maintain CI/CD Pipelines: Design, implement, and manage CI/CD pipelines using GitOps practices. 
• Kubernetes Management: Deploy, manage, and troubleshoot Kubernetes clusters to ensure high availability and scalability of applications. 
• Cloud Infrastructure: Design, deploy, and manage cloud infrastructure on AWS, utilizing services such as EC2, S3, RDS, Lambda, and others. 
• Infrastructure as Code: Implement and manage infrastructure using IaC tools like Terraform, CloudFormation, or similar. 
• Monitoring and Logging: Set up and manage monitoring, logging, and alerting systems to ensure the health and performance of the infrastructure. 
• Automation: Identify and automate repetitive tasks to improve efficiency and reliability. 
• Security: Implement security best practices and ensure compliance with industry standards. 
• Collaboration: Work closely with development, QA, and operations teams to ensure seamless integration and delivery of products. 

Required Skills and Qualifications: 

• Experience: 2-5 years of experience in a DevOps role. 
• AWS: In-depth knowledge of AWS services and solutions. 
• CI/CD Tools: Experience with CI/CD tools such as Jenkins, GitLab CI, CircleCI, or similar. 
• GitOps Expertise: Proficient in GitOps methodologies and tools. 
• Kubernetes: Strong hands-on experience with Kubernetes and container orchestration. 
• Scripting and Automation: Proficient in scripting languages such as Bash, Python, or similar. 
• Infrastructure as Code (IaC): Hands-on experience with IaC tools like Terraform, CloudFormation, or similar. 
• Monitoring Tools: Familiarity with monitoring and logging tools like Prometheus, Grafana, ELK stack, or similar. 
• Version Control: Strong understanding of version control systems, primarily Git. 
• Problem-Solving: Excellent problem-solving and debugging skills. 
• Collaboration: Ability to work in a fast-paced, collaborative environment. 
• Education: Bachelor’s or master’s degree in computer science or a related field. 

Job Description:

Responsibilities

·       Having E2E responsibility for Azure landscape of our customers

·       Managing to code release and operational tasks within a global team with a focus on automation, maintainability, security and customer satisfaction

·       Make usage of CI/CD framework to rapidly support lifecycle management of the platform

·       Acting as L2-L3 support for incidents, problems and service request

·       Work with various Atos and 3rd party teams to resolve incidents and implement changes

·       Implement and drive automation and self-healing solutions to reduce toil

·       Enhance error budgets and hands on design and development of solutions to address reliability issues and/or risks

·       Support ITSM processes and collaborate with service management representatives

Job Requirements

·       Azure Associate certification or equivalent knowledge level

·       5+ years of professional experience

·       Experience with Terraform and/or native Azure automation

·       Knowledge of CI/CD concepts and toolset (i.e. Jenkins, Azure DevOps, Git)

·       Must be adaptable to work in a varied, fast paced exciting, ever changing environment

·       Good analytical and problem-solving skills to resolve technical issues

·       Understanding of Agile development and SCRUM concepts a plus

·       Experience with Kubernetes architecture and tools a plus

• Essentail Skills:
• Docker
• Jenkins
• Python dependency management using conda and pip

• Base Linux System Commands, Scripting
• Docker Container Build & Testing
• Common knowledge of minimizing container size and layers
• Inspecting containers for un-used / underutilized systems
• Multiple Linux OS support for virtual system

• Has experience as a user of jupyter / jupyter lab to test and fix usability issues in workbenches
• Templating out various configurations for different use cases (we use Python Jinja2 but are open to other languages / libraries)
• Jenkins PIpeline
• Github API Understanding to trigger builds, tags, releases
• Artifactory Experience
• Nice to have: Kubernetes, ArgoCD, other deployment automation tool sets (DevOps)

• AWS Cloud, CICD, Serverless setups, Monitoring Setup
• Performance setup, scalability in hands experience, Linux expertise, DevOps Operations

• AWS Cloud, CICD, Serverless setups, Monitoring Setup, Performance setup, scalability in hands experience, Linux expertise, DevOps Operations.