🛠 Tech Stack

💼 About This Role

You'll build and scale core infrastructure for AI-driven industrial inspection and computer vision systems. Your work powers real-world manufacturing deployments and GPU-intensive Kubernetes workloads. The role combines strong software engineering with cutting-edge AI applications.

🎯 What You'll Do

• Design and maintain infrastructure for AI/ML training, deployment, and inference pipelines
• Develop and optimize MLOps systems for computer vision and deep learning models
• Manage GPU-intensive workloads on Kubernetes for performance and cost efficiency
• Build CI/CD pipelines and GitOps workflows with infrastructure-as-code

📋 Requirements

• 5+ years of experience in Platform Engineering, DevOps, or SRE
• Proficiency in Python, Go, JavaScript, or C#
• Hands-on MLOps experience in production environments
• Deep Kubernetes knowledge, including GPU workload orchestration

✨ Nice to Have

• Experience with CNCF ecosystem tools beyond Kubernetes
• Familiarity with observability tooling like Prometheus, Grafana, OpenTelemetry
• Bachelor's/Master's in CS or related field or equivalent experience

🎁 Benefits & Perks

• 🤖 Work on cutting-edge AI and robotics systems for manufacturing
• 💻 High-impact role with real-world industrial deployments
• 📈 Career growth in a rapidly evolving AI and automation field
• 🤝 Collaborative engineering culture focused on innovation

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3System Design· 60 min
4. 4On-site Interviews· Half day