Gen AI Specialist

Role & responsibilities

• Hands-on programming and capabilities in Python, Java, R, or SCALA
• 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 ML 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
• Experience with open source software.

1. Additional Requirements 

• Defining, designing and delivering ML 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 in Enterprise Applications.
• Perform research activities to identify emerging technologies and trends that may affect the Data Science/ ML life-cycle management in enterprise application portfolio
• Basic knowledge in LLM but AI, NLP and deep learning should be strong
• Good with Associate architect or solution architect but no for Technical Lead.
• Hands on experience on Docker. Building docker images with a model and all its dependent packages into the image.
• 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 into large-scale enterprise applications.
• In-depth experience in AI/ML 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/ML stack (Frameworks and tools like MxNET and Tensorflow, 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.