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Quarks Technosoft
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MLOps Engineer

MLOps Engineer

Quarks Technosoft

6 - 10 years

5 - 9 Lacs

Bengaluru

Posted:5 days ago| Platform:

Apply

Skills Required

orchestration Monitoring tools GCP Machine learning Cloud data governance cloud storage Open source SQL Python

Work Mode

Work from Office

Job Type

Full Time

Job Description

">MLOps Engineer

6-10 Years Bengaluru

About the Role

We are seeking a highly experienced and innovative

Senior Machine Learning Engineer

to join our AI/ML team. In this role, you will lead the design, development, deployment, and monitoring of scalable machine learning solutions using

GCP Vertex AI

MLflow

, and other modern ML tools. You ll work closely with data scientists, engineers, and product teams to bring intelligent systems into production that drive real business impact.

Key Responsibilities

Design, develop, and deploy end-to-end machine learning models in production environments using
GCP Vertex AI
Manage the full ML lifecycle including data preprocessing, model training, evaluation, deployment, and monitoring
Implement and maintain
MLflow
pipelines for experiment tracking, model versioning, and reproducibility
Collaborate with cross-functional teams to understand business requirements and translate them into ML solutions
Optimize model performance and scalability using best practices in MLOps and cloud-native architecture
Develop reusable components and frameworks to accelerate ML development and deployment
Monitor deployed models for drift, performance degradation, and retraining needs
Ensure compliance with data governance, security, and privacy standards

Required Skills & Qualifications

6+ years of experience in machine learning engineering or applied data science
Strong proficiency in
Python
,
SQL
, and ML libraries such as
scikit-learn
,
TensorFlow
, or
PyTorch
Hands-on experience with
GCP Vertex AI
for model training, deployment, and pipeline orchestration
Deep understanding of
MLflow
for experiment tracking, model registry, and lifecycle management
Solid grasp of MLOps principles and tools (e.g., CI/CD for ML, Docker, Kubernetes)
Experience with cloud data platforms (e.g., BigQuery, Cloud Storage) and distributed computing
Strong problem-solving skills and ability to work independently in a fast-paced environment
Excellent communication skills and ability to explain complex ML concepts to non-technical stakeholders

Preferred Qualifications

Experience with other cloud platforms (AWS SageMaker, Azure ML) is a plus
Familiarity with feature stores, model monitoring tools, and data versioning systems
Contributions to open-source ML projects or publications in ML conferences

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