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. 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 butAI, 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 aroundAI, ML, deep learning and related technologies. DevelopingAI/ML models in real-world environments and integratingAI/ML using Cloud native or hybrid technologies into large-scale enterprise applications. In-depth experience inAI/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 theAI/ML stack (Frameworks and tools like MxNET and Tensorflow, ML platform such as Amazon SageMaker for data scientists, API-drivenAIServices 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.
