AI & ML

Role Overview: You will be responsible for architecture design and total solution design from requirements analysis to engineering for data ingestion, pipeline, data preparation, and orchestration. Your role will involve applying the appropriate ML algorithms on the data stream and making predictions. Key Responsibilities: - Demonstrating hands-on programming and architecture capabilities in Python, Java, R, or SCALA - 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. - Having strong hands-on experience with statistical packages and ML libraries like R, Python scikit learn, Spark MLlib, etc. - Conducting effective data exploration and visualization using tools like Excel, Power BI, Tableau, Qlik, etc. - Utilizing an extensive background in statistical analysis and modeling including distributions, hypothesis testing, and probability theory. - Working with RDBMS, NoSQL, and big data stores like Elastic, Cassandra, Hbase, Hive, HDFS, and relevant open source systems. - Developing best practices and recommendations surrounding 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. Qualifications Required: - Bachelor's degree or equivalent with a minimum of 6 - 10 years of work experience in roles ranging from Engineer to Architect. Role Overview: You will be responsible for architecture design and total solution design from requirements analysis to engineering for data ingestion, pipeline, data preparation, and orchestration. Your role will involve applying the appropriate ML algorithms on the data stream and making predictions. Key Responsibilities: - Demonstrating hands-on programming and architecture capabilities in Python, Java, R, or SCALA - 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. - Having strong hands-on experience with statistical packages and ML libraries like R, Python scikit learn, Spark MLlib, etc. - Conducting effective data exploration and visualization using tools like Excel, Power BI, Tableau, Qlik, etc. - Utilizing an extensive background in statistical analysis and modeling including distributions, hypothesis testing, and probability theory. - Working with RDBMS, NoSQL, and big data stores like Elastic, Cassandra, Hbase, Hive, HDFS, and relevant open source systems. - Developing best practices and recommendations surrounding 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. Qualifications Required: - Bachelor's degree or equivalent with a minimum of 6 - 10 years of work experience in roles ranging from Engineer to Architect.