As an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers, you will be expected to have the following skills:
5+ years of experience deploying Machine Learning pipelines in large enterprise production systems. - Ability to develop end-to-end ML solutions from business hypothesis to deployment, understanding the entirety of the ML development life cycle. - Expertise in modern software development practices, with solid experience using source control management (CI/CD). - Proficiency in designing relevant architecture/microservices for application integration, model monitoring, training/re-training, model management, model deployment, model experimentation/development, and alert mechanisms. - Experience with public cloud platforms such as Azure, AWS, GCP. - Familiarity with serverless services like lambda, azure functions, and/or cloud functions. - Knowledge of orchestration services like data factory, data pipeline, and/or data flow. - Experience with data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform. - Understanding of data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift. - Proficiency in distributed computing services like Pyspark, EMR, Databricks. - Experience with data storage services like cloud storage, S3, blob, S3 Glacier. - Familiarity with data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik. - Proven experience in serving up predictive algorithms and analytics through batch and real-time APIs. - Strong technical acumen in automated testing. - Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.). - Hands-on experience with statistical packages and ML libraries such as Python scikit learn, Spark MLlib, etc. - Ability to work in cross-functional teams and collaborate effectively with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders. - Proficiency in developing and debugging in languages like Java, Python. - Apply Machine Learning techniques in production, including neural networks, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc. In your role as an ML Architect (Deployments), your responsibilities will include:
Deploying ML models into production and scaling them to serve millions of customers. - Utilizing technical solutioning skills with a deep understanding of technical API integrations, AI/Data Science, BigData, and public cloud architectures/deployments in a SaaS environment. - Managing stakeholder relationships effectively by influencing and managing the expectations of senior executives. - Building and maintaining strong relationships with business, operations, and technology teams internally and externally. - Providing software design and programming support to projects. To qualify for this position, you should be an engineering and post-graduate candidate, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years. As an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers, you will be expected to have the following skills:
5+ years of experience deploying Machine Learning pipelines in large enterprise production systems. - Ability to develop end-to-end ML solutions from business hypothesis to deployment, understanding the entirety of the ML development life cycle. - Expertise in modern software development practices, with solid experience using source control management (CI/CD). - Proficiency in designing relevant architecture/microservices for application integration, model monitoring, training/re-training, model management, model deployment, model experimentation/development, and alert mechanisms. - Experience with public cloud platforms such as Azure, AWS, GCP. - Familiarity with serverless services like lambda, azure functions, and/or cloud functions. - Knowledge of orchestration services like data factory, data pipeline, and/or data flow. - Experience with data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform. - Understanding of data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift. - Proficiency in distributed computing services like Pyspark, EMR, Databricks. - Experience with data storage services like cloud storage, S3, blob, S3 Glacier. - Familiarity with data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik. - Proven experience in serving up predictive algorithms and analytics through batch and real-time APIs. - Strong technical acumen in automated testing. - Extensive backgrou