Senior MLE - MLOps, Python, GCP, VertexAI, GKE

UPS; Chennai District; On-site; INR 800,000 - 1,200,000; Full time

Job summary

UPS in Tamil Nadu is looking for a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform. The role requires proficiency in programming, knowledge of ML algorithms, and experience with tools like Kubernetes and BigQuery.

The ideal candidate will automate ML workflows and ensure model reliability, working closely with technical teams to develop scalable ML infrastructure. A Bachelor’s Degree in a relevant field is required.

Qualifications

• Expertise in programming languages like Python and SQL.
• Understanding of MLOps practices for deploying ML systems.
• Familiarity with various ML models and algorithms.
• Knowledge of model evaluation metrics and monitoring tools.
• Excellent communication skills for documentation.

Responsibilities

• Manage deployment and maintenance of ML models in production.
• Monitor model performance and address issues.
• Optimize queries and pipelines for efficiency.
• Provide regular performance reports to stakeholders.

Skills

Python

SQL

MLOps best practices

Machine Learning algorithms

Model monitoring

Google Cloud Platform tools

Documentation

Education

Bachelor’s Degree in a quantitative field

Tools

Google Cloud Platform

Kubernetes

Vertex AI

BigQuery

Job description

Job Summary

We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production‑ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state‑of‑the‑art machine learning systems that create value for the business.

Responsibilities

• Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems.
• Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias.
• Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback.
• Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders.
• Optimization of the queries and pipelines.
• Modernization of the applications whenever required.

Qualifications

• Expertise in programming languages like Python, SQL.
• Solid understanding of best MLOps practices and concepts for deploying enterprise-level ML systems.
• Understanding of machine learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.).
• Understanding of model evaluation metrics, model monitoring tools and practices.
• Experienced with GCP tools like BigQueryML, MLOps, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc.
• Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications.
• Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining.
• Bachelor’s Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.

Bonus Qualifications

• Experience in Azure MLOps.
• Familiarity with Cloud Billing.
• Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features.
• Experience working in an Agile environment, understanding of Lean Agile principles.

Employee Type

Permanent

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