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.