AI Engineer:

Responsibilities:

The candidate should be able to operate in alignment with the project plan and take ownership of implementing AI solutions based on requirements defined in user stories, features, and DevOps workflows. Responsibilities include, but are not limited to

- Design, train, and deploy AI/ML models for predictive and prescriptive analytics.
- Integrate AI solutions into cloud and edge environments.
- Optimize models for accuracy, efficiency, and scalability.
- Collaborate with data engineers and scientists for data preparation and feature engineering.
- Implement MLOps practices for continuous integration and deployment.

Required Skills:

- Deep expertise in Python especially with PyTorch/TensorFlow (model training, fine-tuning for vision,



text, and audio; Supervised, Unsupervised, Reinforcement learning experience is a plus).
- Demonstrated experience in designing, modifying, developing and implementing AI and Gen AI applications.
- Familiarity with MLOps practices (pipeline automation, CI/CD, MLflow, Docker).
- Knowledge of Machine Learning techniques: anomaly detection, predictive analytics, manufacturing yields.

Experience with OpenUSD APIs for asset management, scene graphs, and data exchange between simulation and analytics stacks.

📌 AI Engineer (India)
🏢 Arminus
📍 India