AI Engineer

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety.

Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance.

As a research scientist at AryaXAI, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability.

Responsibilities

• You'll work on advanced problems related to ML explainability, ML safety, and ML alignment.

• You'll have flexibility in picking up the specialization areas within ML/DL and problem types that address the above challenges.

• Create new techniques around ML Observability & Alignment.

• Collaborate with MLEs and SDE to roll out the features and manage their quality until they are fully stable.

• Create and maintain technical and product documentation.

Qualifications

• Has a solid academic background in concepts of machine learning and deep learning.

• Hands-on experience in working with deep learning frameworks like Tensorflow, Pytorch etc

• Enjoys working on various DL problems that involve using different types of training data sets - textual, tabular, categorical, images etc

• Comfortable deploying code in cloud environments/on-premise environments.

• Strong fundamentals in MLOps and productionising ML models.

• Prior experience on working on ML explainability methods - LRP, SHAPE, LIME, IG, CEM etc.

• 2+ years of hands-on experience in Deep Learning or Machine Learning.

• Hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc.