Associate ML Architect

Job Description

Responsibilities:
 

 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.
 

 Requirements:
 

 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.
 

Good to have skills:
 

Experience with Data Engineering projects-Design & Development on Data Platforms