Senior MLOps Engineer (GCP / CV / Perception Pipelines), Shimoga

Location: Remote (CET working hours) Who we are At Jaipur Robotics, we build AI systems that turn visual and sensor data into automation, efficiency, and operational intelligence for the waste industry. We are a fast-growing, VC-backed clean-tech startup based in Switzerland, working with leading operators across Europe and expanding our engineering team to develop industrial perception and automation systems.What we offer We’re hiring a Senior MLOps Engineer (GCP / Computer Vision&ML Pipelines) to design and operate the infrastructure behind our ML systems. This role focuses on productionizing computer vision and perception pipelines at scale on GCP. You will work across CI/CD, cloud infrastructure, and data pipelines , ensuring models and data systems run reliably at scale.Own ML infrastructure on GCP across multiple production deployments Build and operate end-to-end ML training/inference pipelines Work directly with founders and R&D engineers on core systems Contribute to scaling real-world AI systems used in industrial environments Competitive salary and stock options Key Responsibilities MLOps&CI/CD  Build and maintain CI/CD pipelines using GitHub Actions Automate model training, validation, and deployment workflows Manage versioning of models, datasets, and pipelines Implement safe deployment strategies (rollbacks, staged releases) Cloud / Data Pipelines Deploy and manage serviceson Cloud Run, GCS, Pub/Sub, and data storage systems (SQL / NoSQL / Redis)Build scalable pipelines using Apache Beam / Dataflow Process large-scale image and sensor datasets Ensure reliability through monitoring, observability, and cost-aware design Containerization, Kubernetes&Runtime Build and manage Docker-based services for ML and data pipelines Deploy and manage workloads on Google Kubernetes Engine (GKE) Optimize containers for performance, resource efficiency, and reliability Implement rolling deployments, health checks, and failover strategies Maintain reproducible environments across dev, staging, and prodML Systems&Collaboration Work closely with ML engineers to productionize models Optimize inference pipelines and resource utilization Implement monitoring for model performance and drift Requirements Strong experience with GCP (Cloud Run, GKE, GCS, Pub/Sub, IAM) Experience building CI/CD pipelines (GitHub Actions or similar)Experience with Docker and Kubernetes (GKE) in production Experience building data pipelines (Apache Beam / Dataflow) Solid understanding of ML lifecycle Familiarity with streaming pipelines and real-time systems Experience operating in production with failure handling and debugging Strong programming skills in PythonNice to Have Experience working in an early-stage startup / scale-up ( Experience with camera and LiDAR systems Experience deploying on edge in restricted IT/OT industrial environments Maintain infrastructure using Terraform (infrastructure-as-code) Apply now Use the contact form on or send us an email at