What you will do:

• Defining, designing and delivering ML architecture patterns operable in native and hybrid cloud architectures.
• Research, analyze, recommend and select technical approaches to address challenging development and data integration problems related to ML Model training and deployment.
• Perform research activities to identify emerging technologies and trends that may affect the Data Science/ ML life-cycle management.

What makes you a great fit:

• 10+ years of experience in implementing and deploying ML / AI Solutions.
• Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Hidden Markov Models, Conditional Random Fields, Topic Modeling, Game Theory, Mechanism Design, etc. )
• Strong hands-on experience with statistical packages and ML libraries (e. g. R, Python scikit learn, Spark MLlib, etc. )
• Work experience as Solution Architect/Software Architect/Technical Lead roles
• Experience with open source software.
• Excellent problem-solving skills and ability to break down complexity.
• Ability to see multiple solutions to problems and choose the right one for the situation.
• Excellent written and oral communication skills.
• Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and related technologies.
• Developing AI/ML models in real-world environments and integrating AI/ML using Cloud native or hybrid technologies.
• In-depth experience in AI/ML and Data analytics services offered on Amazon Web Services and their interdependencies.
• Specializes ML platform such as Amazon SageMaker for data scientists, API-driven AI Services like Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications with a simple API call
• Demonstrated experience developing best practices and recommendations around tools/technologies for ML life-cycle capabilities such as Data collection, Data preparation, Feature Engineering, Model Management, MLOps, Model Deployment approaches and Model monitoring and tuning.