Java AWS Kubernetes Devops Ai

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)

Join our product development team at Planview as a Senior Software Engineer I and become a pivotal force on the Viz Core Team. This role offers the unique opportunity to shape and lead the development of data-processing pipelines and APIs that are at the heart of our software solutions. These solutions are designed to streamline and enhance the efficiency of software delivery, resonating deeply with software engineers who strive to build better and faster.

At Planview Viz, which is powered by the innovative “Flow Framework,” you will tackle complex data challenges and develop scalable solutions within an AWS cloud environment. Your efforts will be crucial in revolutionizing how businesses harness and interpret vast amounts of workflow data, transforming it into actionable insights that propel organizational efficiency and effectiveness.

Responsibilities (What you'll do)

• Create and refine powerful data-processing architectures that integrate seamlessly with a diverse array of external tools, enhancing the way software is delivered across industries.
• Drive operational excellence by critically analyzing problems, defining requirements, and devising robust solutions that push the boundaries of technology.
• Lead and inspire a team of talented engineers, promoting a culture of ownership, meticulous attention to quality, and proactive problem-solving.
• Stay at the cutting edge of technology by updating and expanding your team’s knowledge of cloud architectures, data processing, and advanced analytics, ensuring that you and your team remain leaders in technological innovation.
• Make a direct impact on the efficiency and effectiveness of software delivery worldwide through innovation and leadership.

Qualifications (What you'll bring)

Who We’re Looking For

The ideal candidate is a seasoned professional in cloud software development with a strong foundation in data processing and scalable software systems. You thrive in collaborative environments and are passionate about advancing cloud technology and data architecture to new heights. You are a leader who enjoys mentoring, guiding, and inspiring others.

Preferred Qualifications

Skills, Knowledge, and Expertise

• A degree in Computer Science, Engineering, or a related field.
• 6+ years of experience with a modern programming language, with a focus on back-end systems and cloud-based technologies.
• Strong capability in architecting and designing robust, scalable software systems.
• Proficiency in AWS or another popular cloud platform.

Additional Qualifications

• Experience with big-data technologies such as Spark and Apache Kafka.
• Experience with Java (version 17), Scala, or other JVM programming languages.
• Experience with tools such as Amazon Redshift and MongoDB.
• Experience with continuous integration and continuous deployment tools such as GitHub, Jenkins, Travis CI, or similar platforms.
• Proficiency in test-driven development.
• Experience with containers and orchestration tools such as Kubernetes.
• Experience working on remote or distributed teams and projects.

Role: Developer – Java GenAI

Location: Hyderabad / Bangalore

Work Mode: Hybrid

Experience: Relevant experience in Java, GenAI and Microservices

Notice Period: Immediate to 15th October Joiners

Key Skills: Java, Spring Boot, Microservices, Kafka, LLM, RAG, Vector Databases, LangChain/LangGraph, Spring AI, PL/SQL, GCP/AWS, JMS, DevOps, CI/CD, JUnit, Mockito, Oracle/RDBMS, Redis/NoSQL.

Good to Have: Azure, Docker, Kubernetes/PaaS, Python, GitHub Copilot.

Strong understanding of Agile/Scrum with excellent communication, teamwork, organization and problem-solving skills.

Key Responsibilities:

• Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
• Optimize model performance and ensure scalability in production environments.
• Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
• Conduct data analysis to identify trends and insights.
• Implement MLOps practices for versioning, monitoring, and automating ML workflows.
• Research and evaluate new AI/ML techniques, tools, and frameworks.
• Document system architecture, model design, and development processes.

Required Skills:

• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
• Hands-on experience in building and deploying, finetuning ML/DL models in production.
• Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
• Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
• Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
• Strong analytical skills and ability to work with large datasets.
• Excellent communication and problem-solving abilities.
• Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.).
• Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.

Educational Qualification:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred

 The Role 

As a **DevOps Engineer** you'll own the infrastructure and delivery backbone that 

keeps our platform running as we grow. You'll build the CI/CD, cloud infrastructure, and 

observability that let a small, fast-moving team ship confidently — and you'll keep our AI and 

data workloads reliable and affordable at scale. 

This is a hands-on role with real ownership: you won't be maintaining someone else's setup, 

you'll be shaping ours. You'll work closely with the backend, AI/ML, and data teams to make 

deployment boring, incidents rare, and scaling a non-event. --- 

What You'll Own 

**CI/CD & developer experience** 

- Build and maintain fast, reliable CI/CD pipelines so engineers ship multiple times a day with 

confidence. - Make the path from commit to production simple, safe, and repeatable, with sensible 

automated testing, rollbacks, and release controls. 

**Cloud infrastructure & IaC** - Own our cloud infrastructure (AWS/GCP) end to end, managed as code (Terraform or 

similar) — no click-ops. - Design for scale and cost-efficiency as store and conversation volumes grow. 

**Containers & orchestration** - Run our services on containers/Kubernetes: deployments, autoscaling, networking, and 

resource management. - Support the specific needs of AI/ML workloads, including GPU-backed inference and batch 

processing for the speech pipeline. 

**Reliability & observability (SRE)** - Own uptime, performance, and incident response — monitoring, logging, tracing, alerting, 

on-call, and blameless postmortems. - Define and defend SLOs; keep the platform dependable as it scales across clients. 

**Data & pipeline infrastructure** - Support the infrastructure behind large-scale, edge-to-cloud data movement and 

processing (audio ingestion, ASR/AI pipelines, analytics). - Keep data workloads reliable, performant, and cost-aware. 

**Security & compliance** - Bake security into the platform: secrets management, IAM/least-privilege, encryption in 

transit and at rest, network hardening, and vulnerability management. - Support compliance readiness (including India's DPDP Act and enterprise-client security 

requirements) for a product that handles sensitive customer conversations. 

**Cost & scale** - Own cloud cost visibility and optimization; make scaling decisions that balance reliability 

and spend. --- 

What You'll Bring - 6+ years in DevOps, SRE, platform, or infrastructure engineering, running production 

systems at meaningful scale. - Strong hands-on experience with a major cloud provider (**AWS or Azure or GCP**) and 

Infrastructure-as-Code (**Terraform** or equivalent). - Solid experience with **containers and Kubernetes** in production. - Experience building and owning **CI/CD** pipelines (e.g. GitHub Actions, GitLab CI, 

Jenkins, Argo, or similar). 

- Comfort with a scripting/automation language (Python, Go, or Bash) and a strong 

automation-first mindset. - Real experience with **observability** (Prometheus/Grafana, ELK, Datadog, 

OpenTelemetry, or similar) and running incident response / on-call. - A security-conscious approach — secrets, IAM, encryption, and least-privilege as defaults. - Startup temperament: ownership, pragmatism, and a bias to automate and ship. - Based in or willing to relocate to Bangalore, and up for an onsite/hybrid, in-person team. 

Bonus Points - Experience running **ML/AI or GPU workloads** in production (inference serving, batch 

pipelines, model deployment). - Experience with data-intensive infrastructure — streaming/queues (Kafka, SQS), data 

pipelines, or large object/audio storage. - Exposure to **edge devices / IoT fleets**, OTA updates, or high-volume device-to-cloud 

ingestion. - Experience with compliance/security frameworks (SOC 2, ISO 27001, DPDP). - FinOps / cloud cost-optimization experience. - Early-stage startup experience. --- 

Why Join - Own infrastructure that's already live with leading retail brands and growing fast — real 

scale, real impact. - Work across genuinely interesting workloads: speech AI, GPU inference, large-scale data, 

and edge-to-cloud ingestion. - Small team, high ownership, direct line to engineering leadership — your decisions ship. - Build the platform foundation of a category-defining product from an

Job Description:

Pre-requisite skills required for a DevOps Engineer role include:

• 6+yrs exp in DevOps
• Experience working on Linux based infrastructure
• knowledge in AWS, docker, CI/CD tools
• Hands on exp in Python/shell scripting language
• hands on exp in AWS and Azure
• Work exp in Docker, Terraform, Ansible, Kubernetes, LINUX
• Excellent understanding of Ruby, Python, Perl, and Java
• Configuration and managing databases such as MySQL, Mongo
• Excellent troubleshooting
• Working knowledge of various tools, open-source technologies, and cloud services

7+ years in software programming experience with the following 

AIDLC - Experience building Agentic AI agents and be part of POD that uses AI agents for software development lifecycle. 

UI - ANGULAR or REACT worked on any version >2013, JavaScript, HTML, CSS 

API and Backend - Java/J2EE, Spring (Core/Boot/Batch), Rest API, Microservices, UI Widgets, NextJS, Test Automation 

- SQL and GraphQ Engineer L 

- Analytical and debugging capabilities using Splunk Queries, Azure KQL, Grafana, Dynatrace 

Knowledge and hands-on experience with: 

- Cloud: Azure, OpenShift, AKS 

- DevOps: GitHub, GitHub Actions, Artifactory, Docker 

Database - mySQL 

Agile/Scrum experience 

Prior experience in Healthcare industry Utilization management/prior auth or Care management / Optum Clinical Manager experience preferred. 

AI Skills: All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes but is not limited to: 

Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise. 

Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows. 

Continuous Learning: Stay current with evolving AI capabilities and features and apply them to improve delivery quality and velocity. 

Company Overview:

Planview is hiring a DevOps Engineer in Bengaluru, India to support Planview SaaS applications across the product line. You will work in a global, collaborative team — owning CI/CD pipelines, cloud infrastructure, and automation to keep deployments fast and systems reliable.

Responsibilities

• Build and maintain CI/CD pipelines in Jenkins for reliable, fast delivery.
• Manage containerized workloads on Docker and ECS — task definitions, services, and clusters.
• Provision and manage AWS infrastructure using Terraform (CloudFormation a plus).
• Automate configuration and deployment tasks using Python (Ansible a plus).
• Set up and maintain monitoring and alerting via New Relic (CloudWatch, Datadog, or Prometheus/Grafana a plus).
• Write Shell and Python scripts to automate operations and reduce manual work.
• Manage Git workflows — branching, merge strategies, and pull request reviews.
• Administer and support MSSQL databases underpinning the product line — backups, restores, and basic performance troubleshooting.
• Troubleshoot deployment, performance, and infrastructure issues with development teams.
• Participate in on-call rotations and drive incident response.
• Continuously improve infrastructure resilience and deployment speed.
• Apply AI-assisted engineering tools (e.g., GitHub Copilot, Claude Code) to speed up IaC authoring, pipeline debugging, and day-to-day scripting.

Qualifications

Must-Have Skills

• Experience: 4–6 years of experience in DevOps, SRE, or Infrastructure Engineering.
• OS: Linux & Windows administration (systemd, package management, log analysis).
• Cloud: AWS (Active Directory, ECS, EC2, CloudFront, S3, VPC, IAM, RDS, Lambda basics).
• Source Control: Git — branching, merge/rebase, PR reviews.
• CI/CD (Jenkins): Pipeline creation and basic Groovy scripting.
• Containerization (Docker + ECS): Task definitions, services, and clusters.
• IaC: Terraform.
• Monitoring: New Relic.
• Scripting: Bash and Python scripting for automation.
• Networking Basics: DNS, load balancers, security groups, VPNs.
• Logging: ELK stack / CloudWatch Logs.
• Database: MSSQL administration — backups, restores, basic performance troubleshooting.
• Infrastructure Automation: Hands-on experience automating infrastructure provisioning, configuration, and deployment end-to-end.
• AI-Assisted Engineering: Comfortable working with AI coding/DevOps assistants (e.g., GitHub Copilot, Claude Code) for IaC generation, scripting, and troubleshooting — verified via a mandatory AI proficiency assessment during interviews.

Nice-to-Have Skills

•     Configuration Management: Ansible.

•     Additional IaC: CloudFormation.

•     Architecture: Knowledge of microservices architecture.

•     Cloudflare: DNS, CDN, WAF.

•     Artifact Repositories: Nexus, JFrog Artifactory, ECR.

•     Other CI/CD Tools: GitHub Actions.

•     AWS cost optimization / FinOps awareness.

•     Datadog, CloudWatch, or Prometheus/Grafana.

•     AIOps: Exposure to AI-driven anomaly detection, root-cause analysis, or incident triage (e.g., Dynatrace Davis AI, Datadog Bits AI, Harness AIDA).

•     Database Basics: RDS backups, restores, performance tuning.

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

We are looking for an Engineering Lead to own the entire technology stack — from onboarding and underwriting to disbursals, repayments, and collections — and to build the engineering function into something genuinely AI-native. 

What You'll Own 

● Full tech stack: backend, frontend, infrastructure, integrations, and data pipelines 

● Real-time underwriting and decisioning systems 

● LOS/LMS architecture — onboarding, disbursals, repayments, and collections

● Integrations with bureaus, KYC providers, account aggregators, and payment gateways 

● Reconciliation systems — disbursement, repayment, and NACH reconciliation end-to-end 

● AWS infrastructure: scaling, reliability, uptime, and cloud cost ownership ● Data infrastructure for the credit and risk team — feature pipelines, model serving, experiment infrastructure 

● Engineering leadership: hiring, sprint planning, code reviews, and execution standards 

● Compliance systems: RBI guidelines, DPDP, KYC/AML, e-NACH, e-sign 

AI-Native Engineering 

This is a core part of the role, not a bonus. You will build a machine-readable knowledge base of the entire codebase — architecture, data models, service contracts, coding standards, decision history — so that AI agents working on code have the context to produce accurate, consistent output. You will build skills for code review, developer onboarding, and recurring engineering workflows. You will build a code review pipeline where agents do the first pass on every pull request. The knowledge base and the skills improve over time as the team grows and the product evolves. 

What We're Looking For 

● 7+ years in software engineering, with at least 2 years leading teams or architecture

● Strong hands-on experience with Python, Django, and React Native

● Deep expertise in AWS and cloud-native architecture 

● Experience with both SQL and NoSQL databases 

● Strong understanding of distributed systems, microservices, and API design

● Experience owning reconciliation or payment flow infrastructure in a lending or payments context

● Prior experience in fintech / NBFC / digital lending — mandatory

● Strong understanding of the full loan lifecycle — mandatory 

● You have used LLMs seriously as engineering tools and have strong opinions about what makes AI-assisted development produce good output versus mediocre output 

Bonus: Kubernetes / Kafka, AI/ML-driven underwriting, Account Aggregator framework, e-NACH / e-Sign / Video KYC integrations 

What Success Looks Like 

● scales with strong uptime, performance, and reliability

● Reconciliation runs cleanly — no financial discrepancies surface late ● A new engineer joins and is writing standard, correct code within their first week

● The credit team is never blocked on an engineering dependency 

● Engineering health metrics are tracked and visibly improving 

● AI agents are doing the structured first pass on code reviews, and the system gets smarter over time