# MLOps Engineer

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-08-17

**Salary:** INR 1,500,000–3,000,000

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟱𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟯𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟱-𝟯𝟬 𝗟𝗣𝗔)

Experience: 3+ yrs

Location: Bengaluru, Karnataka, India

Job Type: Full-time

We are looking for an experienced **MLOps Engineer** to build, automate, and operate reliable machine learning infrastructure and production deployment workflows. This role focuses on creating scalable ML platforms, streamlining model lifecycle management, and ensuring the reliability, security, and performance of machine learning workloads across **GCP and Azure**.

The ideal candidate will have strong hands-on expertise in **Python, Docker, Kubernetes, Terraform, Airflow, MLflow, Vertex AI, and cloud infrastructure**. You will work closely with data scientists, ML engineers, software engineers, and cloud architects to take machine learning models from experimentation through production while establishing robust automation and monitoring practices.

## Requirements

### Key Responsibilities

-   Build and automate ML workflows using **Apache Airflow / Cloud Composer** for data ingestion, preprocessing, training, and deployment.
-   Manage **MLflow** for experiment tracking, model packaging, versioning, and model registry processes.
-   Design and optimize training environments for machine learning and LLM workloads.
-   Develop scalable model-serving solutions using **FastAPI, Flask, API Gateway**, and high-performance inference endpoints.
-   Manage **Docker, Kubernetes, GKE, and AKS** infrastructure, including auto-scaling GPU/CUDA workloads.
-   Build and maintain **CI/CD pipelines** to automate ML application and model deployments.
-   Monitor model performance, inference latency, data drift, infrastructure health, and production reliability.
-   Work with **Vertex AI Workbench, Model Garden, Feature Store, Vertex AI Pipelines, and BigQuery ML**.
-   Use **Terraform** to provision and maintain secure, scalable, and reproducible cloud infrastructure.
-   Develop production-quality Python applications using modular design, testing, and engineering best practices.
-   Work with **CDC, Spark/PySpark**, and optimize data movement between BigQuery and ML training environments.
-   Implement secure ML infrastructure using **IAM, VPC Service Controls, endpoint security**, and cloud security best practices.
-   Support enterprise-scale ML infrastructure migration and modernization across **GCP and Azure**.

### What Makes You a Great Fit

-   **3+ years of experience** in MLOps, ML Engineering, or a closely related field.
-   Strong hands-on expertise in **Python, Docker, Kubernetes, GKE/AKS, and Terraform**.
-   Practical experience with **Airflow/Cloud Composer and MLflow**.
-   Strong knowledge of **GCP, Azure, Vertex AI, BigQuery ML, and Vertex AI Pipelines**.
-   Experience managing Kubernetes operators and resources for ML workloads.
-   Hands-on experience with **FastAPI/Flask, API Gateway, CI/CD, and model serving**.
-   Strong understanding of **CDC, Spark/PySpark, IAM, and VPC Service Controls**.
-   Experience building and operating production ML platforms with a focus on scalability and reliability.
-   Exposure to **LLMOps, foundation models, prompt versioning, or vector databases** is an advantage.
-   Experience migrating or managing enterprise-scale ML infrastructure across **Azure and GCP** is preferred.
-   Relevant MLOps/ML Engineering certifications and production ML platform experience are a plus.
-   Strong troubleshooting, analytical, communication, and collaboration skills, with a strong ownership mindset.

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/6DCF6E9335/apply)

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