Job Description
<p>Role : Software Solution Architect<br/><br/>Job Responsibilities :<br/><br/>- Define, design, and deliver ML architecture patterns operable in native and hybrid cloud architectures.<br/><br/>- 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.<br/><br/>- Perform research activities to identify emerging technologies and trends that may affect the Data Science/ML lifecycle management in the enterprise application portfolio.<br/><br/>- Lead total solution design from requirements analysis, design, and engineering for data ingestion, pipeline, data preparation & orchestration, applying the right ML algorithms on the data stream and predictions.<br/><br/>Requirements :<br/><br/>- Hands-on programming and architecture capabilities in Python, Java, R, or Scala.<br/><br/>- Minimum 8+ years of experience in enterprise applications development.<br/><br/>- 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.<br/><br/>- Strong hands-on experience with statistical packages and ML libraries (e.g., R, Python scikit-learn, Spark MLlib, etc.).<br/><br/>- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.).<br/><br/>- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.).<br/><br/>- Hands-on experience in RDBMS, NoSQL, big data stores like Elastic, Cassandra, HBase, Hive, HDFS.<br/><br/>- Work experience as a Solution Architect/Software Architect/Technical Lead.<br/><br/>- Experience with open-source software.<br/><br/>- Excellent problem-solving skills and ability to break down complexity.<br/><br/>- Demonstrated technical expertise around architecting solutions around AI, ML, deep learning, and related technologies.<br/><br/>- Developing AI/ML models in real-world environments and integrating AI/ML using Cloud-native or hybrid technologies into large-scale enterprise applications.<br/><br/>- In-depth experience in AI/ML and Data analytics services offered on Amazon Web Services and/or Microsoft Azure cloud solutions and their interdependencies.<br/><br/>- Specializes in at least one of the AI/ML stack (Frameworks and tools like MxNET and TensorFlow, ML platforms 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).<br/><br/>- Demonstrated experience developing best practices and recommendations around tools/technologies for ML lifecycle capabilities such as Data collection, Data preparation, Feature Engineering, Model Management, MLOps, Model Deployment approaches, and Model monitoring and tuning.</p> (ref:hirist.tech)