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# Software Engineer II (AI | Up to 40LPA)

- **Company:** AI AI LLC
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $103,376.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Big Data, Continuous Integration, Fault Tolerance, Github, Python (Programming Language), Machine Learning, OpenCV, Tensorflow, Workflow Management Systems, Data Logging, Pytorch, Delivery Pipeline, Deep Learning, Backend, Gitlab-ci, Kubernetes, HuggingFace, Machine Learning Operations, Software Version Control, Docker, Jenkins
- **Published:** July 31, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p8u00ants8

## About the Role

* 3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering, with exposure to Computer Vision or NLP systems.
 * Hands-on experience with workflow orchestration frameworks (preferably Temporal) for building reliable, fault-tolerant, long-running distributed workflows.
 * Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers.
 * Hands-on experience with Docker, Kubernetes, and containerized ML deployment pipelines in production environments.
 * Experience building and maintaining CI/CD pipelines for ML systems (e.g., Jenkins, GitHub Actions, GitLab CI).
 * Working knowledge of experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC).

## Description

* Own the end-to-end MLOps lifecycle, from model packaging and CI/CD to deployment, monitoring, and rollback for computer vision, NLP, and multi-modal models.
 * Design and maintain scalable training and inference pipelines for large datasets and models, optimizing for cost, latency, and throughput.
 * Build and manage containerized deployment infrastructure (Docker, Kubernetes) for hosted deep learning and geoprocessing services.
 * Set up and maintain experiment tracking, model registry, and versioning systems to ensure reproducibility across the research-to-production lifecycle.
 * Implement model monitoring and observability - drift detection, performance degradation alerts, logging, and dashboards, for models running in production.
 * Apply model optimization techniques (quantization, pruning, knowledge distillation) to improve inference efficiency in production.
 * Collaborate with Research Engineers, Backend Engineers, and Product teams to translate research ideas into deployable, production-ready services.
 * Develop and maintain infrastructure-as-code, monitoring, and logging for all deployed ML/AI software.


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