Punawale Pune Maharashtra Senior devops Jobs – 93 Open Jobs Today

Lead ML Devops Engineer
Dentsu —Kharadi, Pune, Maharashtra
- Full-time
- Computer Science
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Lead Engineer, Systems Engineering - Salesforce Architect
Cencora —Pune, Maharashtra
- Full-time
- Azure
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Junior DevOps/MLOps Developer.
EncureIT Systems Private Limited —Pune, Maharashtra
- ₹1,00,000 - ₹1,20,000 a year
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13d
Principal Enterprise Software Engineer
Wolters Kluwer —Pune, Maharashtra
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Senior AI Engineer
Velsera —Pune, Maharashtra
- Full-time
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- Java
- AWS
- Health insurance
- Paid time off
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Lead Engineer, Systems Engineering
Cencora —Pune, Maharashtra
- Full-time
- Azure
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Senior Software Engineer
Finastra —Pune, Maharashtra
- Full-time
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2d
Site Reliability Engineer
Nice —Pune, Maharashtra
- Remote
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Ethernovia, Inc. —Pune, Maharashtra
- Full-time
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Custom Software Engineer
Accenture —Pune, Maharashtra
- Full-time
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Technical Lead
Barclays —Pune, Maharashtra
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Barclays —Pune, Maharashtra
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Panasonic —Pune, Maharashtra
- Full-time
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Lead ML Devops Engineer

Dentsu -
Kharadi, Pune, Maharashtra

Apply Now

Job details

Full-time

Qualifications

BCS
CI/CD
Computer Science
Kubernetes
Software deployment
Git
Google Cloud Platform
Bachelor's degree
Machine learning
Terraform
Continuous integration
GitHub
APIs
Data science
AI
Jenkins
GitLab
Python
Information Technology

Full job description

Job Description:

Job Title: Lead GCP MLOps Engineer

DCF: L35

Experience: 5 - 8 Years

Role Summary

We are seeking a highly skilled Senior GCP MLOps Engineer to support the deployment, automation, and operationalization of machine learning solutions on Google Cloud Platform (GCP).

The primary focus of this role is to automate the deployment and lifecycle management of Python-based machine learning models developed by business and data science teams. The ideal candidate will possess strong expertise in GCP cloud engineering, MLOps frameworks, CI/CD automation, infrastructure management, and production-grade ML deployment architectures.

This is an engineering-focused role responsible for ensuring machine learning models are deployed, monitored, scalable, secure, and reliable in production environments.

Key Responsibilities

1. MLOps Platform Engineering

Design, build, and maintain scalable MLOps frameworks on Google Cloud Platform.
Automate deployment, testing, monitoring, and lifecycle management of machine learning models.
Establish repeatable and standardized ML deployment processes across environments.
Implement model versioning, artifact management, and deployment governance standards.
Support model retraining, rollback, and release management processes.

2. Machine Learning Deployment & Automation

Deploy Python-based machine learning models into production environments.
Build automated deployment pipelines for batch and real-time inference workloads.
Develop reusable deployment templates and automation frameworks.
Support model serving using Vertex AI Endpoints and containerized deployment architectures.
Ensure high availability, reliability, and scalability of production ML services.

3. CI/CD & Infrastructure Automation

Design and implement CI/CD pipelines for machine learning applications and services.
Integrate source control, testing, and deployment workflows into enterprise delivery pipelines.
Implement Infrastructure-as-Code (IaC) practices for repeatable environment provisioning.
Support environment management across development, testing, and production environments.

4. Cloud Engineering & Platform Operations

Design and support cloud-native ML infrastructure on GCP.
Manage and optimize services including:
- Vertex AI
- Cloud Storage
- BigQuery
- Cloud Build
- Cloud Run
- Kubernetes Engine (GKE)
- Pub/Sub
Optimize infrastructure for performance, reliability, security, and cost efficiency.
Troubleshoot production issues and support platform stability initiatives.

5. Monitoring, Observability & Governance

Implement monitoring and alerting frameworks for deployed machine learning services.
Track model performance, operational health, latency, and system utilization.
Support model lifecycle governance and operational compliance requirements.
Establish logging, observability, and operational dashboards.
Drive best practices for production support and operational excellence.

Technical Expertise Required

Area

Skills / Technologies

Cloud Platform

Google Cloud Platform (GCP)

MLOps

Vertex AI, Model Deployment, Model Monitoring, ML Lifecycle Management

Programming

Python

CI/CD

Cloud Build, GitHub Actions, Jenkins, GitLab CI/CD

Infrastructure Automation

Terraform, Infrastructure-as-Code

Data Platforms

BigQuery, Cloud Storage

Messaging & Integration

Pub/Sub, APIs

Monitoring & Observability

Cloud Monitoring, Logging, Alerting

Version Control

Git, GitHub

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline.
5 - 8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering.
Strong hands-on experience with Google Cloud Platform (GCP).
Proven experience deploying and operationalizing Python-based machine learning models.
Strong experience with Vertex AI and production ML deployment patterns.
Experience building CI/CD pipelines for machine learning applications.
Experience implementing Infrastructure-as-Code using Terraform or similar tools.
Experience monitoring and supporting production machine learning workloads.
Strong troubleshooting and problem-solving skills.

Preferred Qualifications

Google Cloud Professional Machine Learning Engineer Certification.
Familiarity with MLflow, Kubeflow, or similar MLOps frameworks.

Location:

DGS India - Pune - Kharadi EON Free Zone

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent

Apply Now

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senior devops jobs in punawale pune maharashtra

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