About the Role

We are looking for an MLOps Engineer with 3–5 years of experience (including 0–3 years in AI/ML engineering) to help build, deploy, and maintain our machine learning solutions. This role focuses on deploying AI models to edge devices and integrating them with our C++ backend systems. You will work closely with ML, C++, and embedded engineering teams to ensure smooth and reliable production operations.

Key Responsibilities

• Deploy and optimize ML models on edge devices (ARM boards, Jetson, etc.).
• Convert and package models for C++ integration (ONNX, TensorRT, TFLite).
• Automate ML workflows including training, testing, and deployment.
• Build and maintain CI/CD pipelines for Python and C++ components.
• Monitor model performance on devices—latency, drift, accuracy, resource usage.
• Support data pipelines for collecting field data from edge devices.
• Manage model versioning, tracking, documentation, and release processes.

Required Skills

• Strong Python skills and working knowledge of C++ integration.
• Experience with deploying models on edge/embedded hardware.
• Familiarity with ONNX Runtime, TensorRT, TFLite, or similar optimization tools.
• Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins).
• Understanding of ML workflows and model lifecycle.
• Good communication and documentation skills.
• Experience with real-time systems or embedded environments.
• Knowledge of lightweight orchestrators.
• Experience building telemetry/monitoring for edge devices.

What You Will Achieve

• Reliable, fast, hardware-optimized ML deployments on edge devices.
• Smooth integration of ML models with C++ backend systems.
• Automated, repeatable, and stable ML operations across the full lifecycle.