Responsibilities
Defining, designing and delivering Client 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 Client Model training and deployment in
Enterprise Applications.
Perform research activities to identify emerging technologies and trends that may affect the
Data Science/ Client life-cycle management in enterprise application portfolio
Requirements
Hands-on programming and architecture capabilities in Python, Java, R, or SCALA
Minimum 6+ years of Experience in Enterprise applications development (Java, . Net)
Experience in implementing and deploying
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 Client libraries (e. g. R, Python scikit
learn, Spark MLlib, etc. )
Experience in effective data exploration and visualization (e. g. Excel, Power BI, Tableau, Qlik,
etc. )
Extensive background in statistical analysis and modeling (distributions, hypothesis testing,
probability theory, etc. )
Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra, Hbase, Hive,
HDFS
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, Client, deep learning
and related technologies.
Developing AI/Client models in real-world environments and integrating AI/Client using Cloud native
or hybrid technologies into large-scale enterprise applications.
In-depth experience in AI/Client and Data analytics services offered on Amazon Web Services
and/or Microsoft Azure cloud solution and their interdependencies.
Specializes in at least one of the AI/Client stack (Frameworks and tools like MxNET and Tensorflow,
Client 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 Client life-cycle capabilities such as Data collection, Data preparation,
Feature Engineering, Model Management, MLOps, Model Deployment approaches and Model monitoring and tuning.
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Last updated on Oct 18, 2023