Wattmonk Technologies

Job Responsibilities :

- Define, design, and deliver ML 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 ML model training and deployment in enterprise applications.

- Perform research activities to identify emerging technologies and trends that may affect the Data Science/ML lifecycle management in the enterprise application portfolio.

- 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.

Requirements :

- Hands-on programming and architecture capabilities in Python, Java, R, or Scala.

- Minimum 8+ years of experience in enterprise applications development.

- 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.

- Work experience as a Solution Architect/Software Architect/Technical Lead.

- Experience with open-source software.

- Excellent problem-solving skills and ability to break down complexity.

- 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 solutions and their interdependencies.

- 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).

- 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.