Senior AI Platform / MLOps Engineer

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 

Greetings!

Hiring For Large Product Based Company!

Role- Mlops Engineer

Experience- 8-12 years

Location- Pune, Nagpur

JD-

• 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  

Educational Qualification:• B.Tech / M. Tech / M.S. in Computer Science, Data Engineering, AI or related discipline.• Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, AzureDevOps Expert, GCP Professional ML Engineer) is highly desirable.• Contributions to MI-Ops or DevOps open source projects is preferred

Experience:• 7—10 years in machine learning operations or DevOps engineering.• Minimum 4 years building CVCD pipelines for AI/ML model deployment in enterprise orgovernment ecosystems.• Proven experience with containerized and microservice architectures.

Key Responsibilities:1.Design and manage continuous integration and delivery (CVCD) pipelines for AIIMLmodels across multiple environments.2. Establish model versioning, deployment, monitoring, and rollback mechanisms to ensurestability and traceability.3.Automate training, testing, and serving workflows using containerized solutions.4.Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.5. Collaborate with Data Science and Engineering teams to standardize model input/outputformats and performance metrics.6.Implement logging, monitoring, and alerting for deployed models to ensure highavailability and accuracy over time.7.Ensure compliance with Responsible Al guidelines for deployment, including bias auditingand explainability tracking.

• Technical Competencies:• MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP VertexAl Pipelines for end-to—end ML workflow orchestration• Containerization: Docker, Kubernetes, Helm charts, container registries, and microservicesarchitecture for ML workloads• CI/CD: Jenkins, GitLab CI, GitHub Actions, Azure DevOps with specialized ML pipelineintegration and automated testing• Infrastructure-as-Code: Terraform, CloudFormation, Ansible for reproducible MLinfrastructure provisioning and management• Cloud Platforms: AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, BlobStorage), GCP (GKE, Cloud Build, Cloud Storage)• Model Serving: TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.• Programming Languages: Python for automation, Bash scripting, YAML for configurationmanagement, basic understanding Of Go/Java• Database & Storage: Feature stores (Feast, Tecton), model registries, data versioning(DVC), and distributed storage systems• Workflow Orchestration: Apache Airflow, Prefect, Argo Workflows for complex MLpipeline scheduling and dependency management.

• 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  

• Senior Machine Learning Engineer – Vertex AI
• Senior Generative AI Engineer – GCP
• Google Cloud AI/ML Engineer
• MLOps Engineer – Vertex AI
• LLM Engineer – RAG, Gemini, and Vertex AI

Experience: 4+ Years 

Job Title: DevOps Engineer / Cloud Engineer 

Job Summary 

We are looking for an experienced DevOps & Cloud Engineer with 4+ years of experience in cloud infrastructure management, Kubernetes administration, CI/CD pipeline automation, and containerization. The ideal candidate should have hands-on experience with Azure cloud services, Azure Kubernetes Service (AKS), Azure DevOps, Jenkins, Docker, Terraform, and infrastructure automation. 

The candidate will be responsible for managing cloud environments, automating application deployments, maintaining Kubernetes clusters, monitoring system performance, and ensuring the reliability, scalability, security, and availability of applications across development, testing, and production environments. 

Key Responsibilities 

• Design, deploy, and manage cloud infrastructure using Microsoft Azure services, including Virtual Machines, Virtual Networks, Storage Accounts, Load Balancers, Key Vaults, and Resource Groups. 
• Manage and administer Azure Kubernetes Service (AKS), Azure Arc-enabled Kubernetes, and Kubernetes clusters. 
• Develop, configure, and maintain CI/CD pipelines using Azure DevOps and Jenkins for automated application builds, testing, and deployments. 
• Containerize applications using Docker and deploy microservices-based applications on Kubernetes. 
• Manage Kubernetes resources, including Pods, Deployments, StatefulSets, Services, Ingress, ConfigMaps, Secrets, and Namespaces. 
• Automate infrastructure provisioning and configuration management using Terraform, Ansible, and Infrastructure as Code (IaC) practices. 
• Implement Kubernetes networking, load balancing, Role-Based Access Control (RBAC), autoscaling, and resource optimization. 
• Monitor infrastructure and application performance using Prometheus and Grafana. 
• Troubleshoot application, deployment, networking, container, and infrastructure-related issues and perform root cause analysis. 
• Implement disaster recovery procedures, backup validation, failover testing, patch management, and environment upgrades. 
• Manage source code repositories, branching strategies, pull requests, code merges, and version control using Git, GitHub, and Azure Repos. 
• Maintain cloud infrastructure security, compliance, governance, high availability, and operational reliability. 
• Collaborate with development, infrastructure, and security teams to implement DevOps best practices and improve deployment efficiency. 

Mandatory Technical Skills 

1. Cloud Platforms: Microsoft Azure, Azure Virtual Machines, Virtual Networks, Storage Accounts, Load Balancers, Key Vaults. 
2. Containerization & Orchestration: Docker, Kubernetes, Azure Kubernetes Service (AKS). 
3. CI/CD & Automation: Azure DevOps, Jenkins, CI/CD pipeline development and maintenance. 
4. Infrastructure as Code: Terraform; exposure to Ansible and automated configuration management. 
5. Monitoring & Version Control: Prometheus, Grafana, Git, GitHub, Azure Repos. 

Preferred Skills 

• Azure Arc-enabled Kubernetes. 
• VMware Tanzu Kubernetes Grid (TKG). 
• Kubernetes Service Mesh, Ingress Controllers, and Horizontal Pod Autoscaling (HPA). 
• RHEL Linux administration and Shell Scripting. 
• Groovy scripting and SQL fundamentals. 
• Disaster recovery, incident management, and production support. 
• Experience supporting microservices architectures and application migration activities. 

Qualifications 

• Bachelor’s degree in Engineering (B.E.) or an equivalent qualification. 
• 4+ years of experience in DevOps engineering, cloud infrastructure, Kubernetes administration, and deployment automation. 
• Strong troubleshooting, analytical, and problem-solving skills. 
• Ability to work collaboratively with cross-functional teams and manage infrastructure-related incidents. 

Key Deliverables 

• Reliable, scalable, and secure cloud infrastructure. 
• Automated and efficient CI/CD pipelines. 
• Stable Kubernetes clusters and containerized application deployments. 
• Reduced infrastructure provisioning and deployment time through automation. 
• Effective monitoring, incident resolution, and disaster recovery readiness. 

Role Overview: Take full ownership of infrastructure architecture, scaling, and uptime. Establish the technical roadmap, shape and formalize the SRE function, and coach engineers. Design, deploy, and run AI-scale infrastructure that enables real-time healthcare workflows.

Serve as a Founding Member, owning end-to-end infrastructure capabilities and introducing AI-first DevOps, SRE, and MLOps best practices, with direct collaboration alongside the Founder, Co-Founder, and Head of Engineering.

Key Responsibilities:

Architecture & Platform

• Design scalable, secure, cost-efficient AWS/GCP architectures for AI and healthcare workloads
• Lead IaC standards and reusable modules org-wide; own GitOps practices
• Architect optimized Kubernetes (EKS/GKE) for HA, scale, and GPU workload orchestration

Reliability Engineering

• Define and drive reliability targets (SLOs/SLIs); establish SRE practice
• Lead incident management, RCA, and blameless post-mortems for critical systems
• Partner on infrastructure hardening and compliance (SOC 2, HIPAA)

AI-First Engineering

• Lead AI/ML inference and GPU infrastructure scaling in production
• Evaluate and introduce AI tooling, platforms, and automation best practices
• Contribute to LLM deployment pipelines and AI systems reliability

Leadership & SDLC

• Mentor L1/L2 engineers; influence cross-team infrastructure decisions
• Drive DevSecOps maturity across the SDLC (policy-as-code, SBOM, supply-chain security)
• Evaluate and introduce new tooling and platforms

Key Requirements:

• 4–8 years infrastructure/platform/DevOps/SRE
• Deep expertise in AWS & GCP large-scale production systems
• Advanced Kubernetes (cluster design, multi-tenant ops, resource quotas)
• AI/ML inference or GPU infrastructure scaling (production track record)
• Strong Terraform / equiv. and GitOps practices
• Python, Go, or Bash scripting and automation
• Track record in scale, reliability, and cost efficiency
• Lead incident response and on-call practices

Nice-to-Have

• LLM Gateway architecture and optimization
• Zero-trust networking and advanced security/compliance (SOC 2, HIPAA)
• DevSecOps maturity: SBOM, supply-chain security, policy-as-code
• Open-source contributions or platform-team leadership

Why This Role?

• Build infrastructure and products powering real-world healthcare AI
• Work with AI-native engineering teams using cutting-edge tools (Claude Code, Cursor, Copilot)
• Mission-driven impact — improve patient outcomes and save thousands of staff hours
• High ownership, rapid learning, and significant career upside
• Global collaboration across India and the US
• Seed-stage growth — scale from Seed to Series A alongside the team
• Founding Member role and own the entire infrastructure capabilities, including bringing best AI-first practices in DevOps, SRE and MLOps; direct access to work with the Founder, Co-Founder and Head of Engineering

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

AWS / Kubernetes / OpenShift / Linux –

Location: Bangalore

Experience: 7–10 Years

Mandatory Skills:

• Strong hands-on experience with AWS
• Experience in Kubernetes & OpenShift
• Strong knowledge of Linux administration
• Experience with Docker & containerization
• Knowledge of CI/CD pipelines and DevOps practices
• Troubleshooting, monitoring, and deployment experience

Role: Cloud/DevOps Engineer – AWS, Kubernetes & OpenShift

Role Overview

We are looking for a DevOps / Platform Engineer with 3–4 years of experience to help manage AWS cloud infrastructure, automation, and AI-ready platform operations. You will work closely with engineering and AI teams to ensure scalable, secure, and reliable deployments.

Key Responsibilities

1. AWS & Infrastructure: Manage core AWS services (EC2, EKS, Lambda, RDS, S3, VPC) using Infrastructure-as-Code (Terraform, CloudFormation, or CDK).
2. AI/ML Workflows: Support infra for AI applications, model serving, vector databases, and GPU-based workloads.
3. CI/CD & Automation: Build and maintain deployment pipelines using GitHub Actions, GitLab CI, or Jenkins.
4. Containers & Kubernetes: Deploy and maintain containerized applications using Docker and Kubernetes (EKS) or ECS.
5. Observability & Incident Support: Maintain dashboards and alerts with CloudWatch, Prometheus, or Grafana, and participate in on-call response.
6. Security & FinOps: Apply AWS security best practices (IAM, encryption, secrets) and assist with cloud cost optimization.

Must-Have Skills & Qualifications

1. 3–4 years of experience in DevOps, SRE, or Cloud Infrastructure Engineering.
2. AWS & Containers: Good working knowledge of core AWS services, Docker, and Kubernetes (EKS).
3. Infrastructure-as-Code: Practical experience with Terraform, CloudFormation, or AWS CDK.
4. Linux & Networking: Solid fundamentals in Linux systems, DNS, routing, and load balancing.
5. Scripting: Hands-on scripting proficiency in Python, Bash, or Go.
6. CI/CD & Observability: Experience building CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins) and using monitoring tools (CloudWatch, Prometheus, Grafana, or Datadog).

Nice-to-Have Skills

1. Basic exposure to AI/ML infrastructure, model serving, or GPU-based compute.
2. Experience with event-driven messaging systems (Kafka, Redis, or SQS).
3. Familiarity with GitOps tools and Helm (ArgoCD, Flux, or OpenTelemetry).
4. AWS Certifications (Solutions Architect Associate, SysOps Administrator, or DevOps Engineer).