Senior Azure Data / MLOps / AI Platform Engineer

• 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  

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  

Title                                 : Senior AI Platform / MLOps Engineer

Experience                    : 6+ years

Work type                      : Chennai - Work from Office/other locations - Remote

Employment Type      : Full Time

Notice Period              : Immediate

Work Day                     :Mon to Fri

Key Responsibilities:

• Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control-plane topologies
• Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management
• Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression
• GitOps CI/CD, container registry, artifact/model versioning, environment promotion; observability and audit wiring (Splunk, Prometheus/Grafana)
• Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline
• Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

Technical Skills:

• 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred
• Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM — you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory-bandwidth trade-offs
• GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks
• Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

• Azure or AWS GPU compute operations experience

Strongly preferred

• NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards 

Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent experience).

3+ years of hands-on experience with Microsoft Azure, including IaaS, PaaS, networking, identity (Entra ID), and governance services.

2+ years of experience with DevOps tooling and practices, including CI/CD pipelines (Azure DevOps or GitHub Actions), Infrastructure as Code (Terraform, Bicep, or ARM), and version control (Git).

2+ years' experience in infrastructure design, platform engineering, or architecture.

Strong proficiency with automation and scripting — PowerShell, Azure CLI, Python, or Bash.

Solid understanding of containerisation (Docker) and familiarity with orchestration platforms (Azure Kubernetes Service).

Deep understanding of cloud architecture, deployment patterns, and management best practices, including the Azure Well-Architected Framework.

Experience defining standards, governance frameworks, and reference architectures for cloud platform consumption.

Strong knowledge of networking concepts, storage configurations, virtualisation technologies, and disaster recovery/backup design for hybrid environments.

Experience with Azure DevOps (Repos, Pipelines, Boards, Artifacts) or equivalent platforms.

Familiarity with identity and access management (IAM), including Entra ID, RBAC, PIM.

Strong understanding of security, compliance requirements, and IT governance frameworks (ITIL, COBIT, or similar).

Preferred

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 

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.

Tasks & Responsibilities: (Major task and role person will have to perform – list all the relevant points)    

Together with the Business Cloud Services team, you will design, deploy, and operate a cloud‑native Kubernetes PaaS platform on Azure, ensuring high availability, security, and operational excellence.

Your main duties include:

·      Drive cloud‑native automation and self‑service capabilities, ensuring platform security, availability, and disaster recovery readiness

·      Design, operate, and continuously improve Kubernetes clusters and PaaS environments on Azure / AKS

·      Collaborate closely with development teams on deployment strategies, operational requirements, and platform integration

·      Review and maintain infrastructure‑as‑code configurations, CI/CD pipelines, and cloud‑native deployments

·      Build and maintain CI/CD pipelines using Azure DevOps

·      Implement and operate infrastructure‑as‑code solutions using Terraform, Helm, and Ansible

·      Manage containerized workloads and orchestration across cloud‑native environments

·      Set up, operate, and enhance monitoring, logging, and alerting stacks using tools such as Prometheus and Grafana

·      Coordinate with support teams on platform incidents, escalations, and post‑incident analysis

·      Contribute to incident response, continuous improvement, and operational stability

·      Support the development and refinement of DevOps practices, runbooks, and operational standards

·      Actively contributes to improving developer experience and platform reliability

Education & Trainings + Certifications: (Basic & Professional)

·      Bachelor’s degree (Preferably engineering graduate)

·      Azure Solutions Architect Expert Certification is a plus.

·      (optional) ITIL Foundation 

Technical know-how: (Mention the technology on which person must be experienced)

Mandatory

o  Bachelor’s degree in computer science, Information Systems, or a related field

o  5+ years of relevant professional experience in DevOps, Cloud Engineering, or Platform Engineering roles

o  Strong Linux system administration skills for operating production‑grade cloud‑native platforms

o  Proven experience with Microsoft Azure, including AKS and Azure DevOps tooling

o  Strong expertise in Kubernetes, including cluster operations, RBAC, networking, and storage

o  Hands‑on experience with container technologies such as Docker and container registries

o  Proficiency with Terraform, Helm, Ansible, and PowerShell

o  Experience building and operating CI/CD pipelines using Azure DevOps or equivalent tools

o  Cloud‑native monitoring and observability experience using Prometheus, Grafana, and alerting pipelines

o  Solid understanding of security and compliance requirements such as PCI DSS and SOC2

o  Scripting skills for automation and tooling (Bash, Python, PowerShell)

o  Practical experience using Git for version control

o  Excellent communication skills in English (Spanish is a plus)

o  Automation‑first mindset and strong collaboration skills within Agile teams

Good to know

Principles of Agile Software Development.

·      GitHub Actions, GitLab CI, Jenkins, Azure DevOps

·      Containers: Docker, Kubernetes (EKS / AKS / GKE)

·      Configuration Management: Ansible

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. 

8-12 years experience on Data Engineering role working with Databricks & Azure Cloud technologies.

• Bachelor’s degree in computer science, Information Technology, or related field.

• Strong proficiency in PySpark, Python, SQL.

• Strong experience in data modeling, ETL/ELT pipeline development, and automation • Hands-on experience with performance tuning of data pipelines and workflow

• Proficient in working on Azure cloud components Azure Data Factory, Azure DataBricks, Azure Data Lake etc.

• Experience with data modeling, ETL processes, Delta Lake and data warehousing.

• Experience on Delta Live Tables, Autoloader & Unity Catalog.

Preferred - Knowledge of the insurance industry and its data requirements.

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

• Excellent communication and problem-solving skills to work effectively with diverse teams

• Excellent problem-solving skills and ability to work under tight deadlines. 

Key Skills:

Python & System Design: Strong expertise in Python and system design, with proven experience architecting scalable, production-grade systems.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Cloud & Data Architecture: Deep understanding of data and cloud architecture across AWS and Azure, including multi-store data design, distributed systems, streaming, security, and cost optimization.

Azure Databricks: Hands-on production experience with Azure Databricks architecture, including Unity Catalog, Delta Lake, cluster policy design, job orchestration, and Terraform provisioning.

Supply Chain & Inventory Management: Strong domain knowledge of supply chain and inventory operations, including demand planning, MRP logic, inventory parameters, and translating business planning decisions into data requirements.

System Architecture & Technical Decision-Making: Ability to make sound architectural decisions based on first principles, evaluate technical trade-offs, and avoid unnecessary complexity and abstraction.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Technical Leadership: Ability to influence teams without direct authority, establish technical standards, prepare technical specifications and documentation, and mentor team members.

Experience in pharmaceutical or regulated industries is highly valued.