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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 solid 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.
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/PySp

📌 MLOps Engineer (Bengaluru)
🏢 Weekday AI
📍 Bengaluru