Senior AI Platform / MLOps Engineer

AI Platform Engineer, OpenShift / NVIDIA (Freelance)

Positions: 1

Experience: Ideally 6-10 years.

Mission

Own deployment and operation of the underlying enterprise AI/model-serving platform.

Mandatory capabilities

• Kubernetes
• Red Hat OpenShift
• Containers/Docker/OCI
• Helm
• Linux
• CI/CD
• GitOps
• Networking
• Secrets/certificates

Registries

• Monitoring/observability

Priority AI infrastructure experience

• OpenShift AI
• KServe
• NVIDIA GPU Operator
• NVIDIA NIM
• NVIDIA AI Enterprise
• GPU inference
• Open-weight LLM deployment
• vLLM or comparable serving engines
• GPU monitoring and performance optimization

Candidates should understand practical inference concepts including tokens/sec, TTFT, prefill vs decode, context length, KV cache, prefix caching, batching, concurrency, GPU memory, quantization and model routing.

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Get to Know Us:

At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.

Equal Opportunity Employer:

Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. 

If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

Job application link : https://grnh.se/z7qx2ehx1us

Position Overview 

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems. 

Responsibilities 

• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms 
• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning 
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection 
• Deploy, maintain and monitor containerized ML workloads 
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows 
• Implement AI-driven data validation, schema and concept drift detection and metadata management. 
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability 
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention 
• Develop automated test suites for model performance, regression, edge cases and bias validation 
• Monitor model KPIs (accuracy, precision, recall, latency, calibration) 
• Ensure reproducability of experiments and production models 
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption 

Qualifications 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field 
• 5+ 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 

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)

About Us:

CLOUDSUFI, a Google Cloud Premier Partner, is a global leading provider of data-driven digital transformation across cloud-based enterprises. With a global presence and focus on Software & Platforms, Life sciences and Healthcare, Retail, CPG, financial services and supply chain, CLOUDSUFI is positioned to meet customers where they are in their data monetization journey.

Our Values

We are a passionate and empathetic team that prioritizes human values. Our purpose is to elevate the quality of lives for our family, customers, partners and the community.

Equal Opportunity Statement

CLOUDSUFI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified candidates receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation and national origin status. We provide equal opportunities in employment, advancement, and all other areas of our workplace. Please explore more at https://www.cloudsufi.com/

About the role

CloudSufi is hiring an AgentOps Engineer to own the runtime, infrastructure, and operational monitoring for an AI agent testing platform deployed for an enterprise client. You will deploy and instrument agents for testing, set up observability and continuous evaluation on live traffic, and manage the cloud infrastructure the platform runs on.

Responsibilities:

- Deploy and instrument AI agents for testing and monitoring.

- Set up observability — traces, logs, and metrics — and continuous evaluation on production traffic.

- Build drift detection and alerting so quality regressions are caught early.

- Own the cloud infrastructure and CI/CD automation supporting the platform.

- Manage environments, access, and operational reliability.

Required qualifications:

- 5+ years in DevOps, MLOps, or platform engineering.

- Strong Google Cloud experience (Cloud Run, Cloud Build, Pub/Sub, Cloud Logging and Monitoring, IAM) or equivalent cloud platform.

- Experience with CI/CD automation and infrastructure as code.

- Familiarity with deploying LLM or AI agent workloads.

Preferred qualifications:

- Experience with OpenTelemetry and AI/agent observability.

- Exposure to Vertex AI, Gemini Enterprise Agent Platform, or ADK.

Hiring for AI Engineer

Exp: 6 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skill Set:

- Total experience ranging from 6–8 years in software engineering/AI roles

- Min 5 years strong programming experience in Python is a MUST

- Min 3.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

- Experience with cloud platforms (AWS/Azure/GCP)

Key Skills:

• Bachelor's or Master's degree in Computer Science or related field.

• Minimum 5 years of experience in Platform Engineering, DevOps, or Cloud Infrastructure Engineering.

• Experience migrating data and systems between AWS IaaS and PaaS.

• Experience operating and supporting applications using AWS VPC, EKS, and related services for multi-account operations.

• Experience developing fast and reliable Continuous Integration/Continuous Deployment (CI/CD) workflows used by hundreds of application teams.

• Experience administering and troubleshooting Operating Systems such as Linux, Windows, and MacOS.

• Professional Certifications in AWS Networks, CNCF Technologies, or Kubernetes.

• Experience using and configuring observability tools such as ELK, Prometheus/Grafana, AWS CloudWatch, and Jaeger.

• Experience of applied GitOps principles using ArgoCD or Flux.

• Public examples of code you've worked on with other people using any of these technologies:

o Configuration management/Infrastructure as Code (IAC) tools, such as AWS CDK, AWS CloudFormation, Terraform, Ansible, or Puppet.

o Systems solutions in one or more programming languages, such as Golang, Python, Java.

o Build, Release, Deploy or Ops Workflows using Bamboo, Argo Project, or GitHub Actions.

We are looking for an experienced DevOps Engineer to take ownership of production infrastructure, cloud environments, Kubernetes platforms, and infrastructure automation. This is a hands-on role for someone who enjoys solving complex infrastructure challenges and is comfortable being responsible for systems in production.

Key Responsibilities

• Own and operate production infrastructure, including participating in an on-call rotation and responding to production incidents.
• Design, operate, and continuously improve Kubernetes clusters in production.
• Manage and automate infrastructure using Infrastructure as Code, primarily with Terraform.
• Build, maintain, and optimise cloud infrastructure across AWS, GCP, or Azure.
• Work extensively with Linux, including system administration, networking, troubleshooting, and system-level configuration.
• Manage production deployment and GitOps workflows using ArgoCD.
• Improve infrastructure reliability, scalability, security, monitoring, and operational efficiency.
• Troubleshoot complex production issues and drive problems through to resolution.
• Develop automation and processes that reduce manual operational work.
• 
  

Essential Requirements

• 4+ years of hands-on experience operating production infrastructure, with personal ownership and responsibility for live systems, including on-call experience.
• Deep, hands-on Kubernetes experience — you must have operated and managed Kubernetes clusters, rather than simply deploying applications onto clusters managed by another team.
• Strong experience with Infrastructure as Code, with Terraform strongly preferred. Experience with Pulumi or CloudFormation is also considered.
• Strong experience with at least one major cloud platform, ideally AWS. Strong GCP or Azure experience is also welcome, provided you are willing to work with AWS.
• Strong Linux skills and confidence working from the command line, including networking, troubleshooting, system configuration, and performance issues.
• Production experience with ArgoCD and GitOps-based deployment workflows.
• Strong troubleshooting and problem-solving skills, with the ability to take ownership of production incidents and infrastructure issues.

Nice to Have

Experience with email infrastructure would be a strong advantage, particularly:

• Exim
• IMAP / SMTP
• Postfix
• Dovecot
• General mail server administration and maintenance

We are seeking a highly skilled and experienced DevOps Engineer to join our development team.

Years of experience needed: 8+ Yrs

Required Skills

Strong experience with GitLab, TeamCity, Terraform, Kubernetes and Docker

Proficiency in various deployment strategies and CI/CD pipeline setups

Solid understanding of Linux administration and Windows IIS.

Advanced scripting skills in bash and PowerShell

Hands-on experience with Google Cloud Platform (GCP) services, particularly GKE, GCS, GCE, Cloud SQL, and load balancers

Familiarity with Autosys for job scheduling and automation

Excellent problem-solving and troubleshooting skills

Ability to work independently and collaboratively in a fast-paced environment

• • Strong communication and interpersonal skills.

Build production-grade cloud infrastructure that powers enterprise applications with cutting-edge DevOps practices.

What you'll do:

• Design CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI)
• Containerize apps with Docker, deploy on Kubernetes clusters
• Manage infrastructure as code (Terraform, CloudFormation)
• Set up monitoring (Prometheus, Grafana, ELK Stack)
• Cloud migrations (AWS EC2, EKS, RDS → GCP equivalent)
• Optimize costs and performance for live production systems

What we need:

• Basic Python/Bash scripting
• Docker basics, Git workflows
• Cloud exposure (AWS/GCP/Azure free tier projects)
• Problem-solving mindset, eagerness to learn

Real impact:

• Deploy apps used by 1000+ daily users
• Work with senior DevOps engineers on client deliverables
• Build portfolio for FAANG-level interviews