LLM Ops/ML Sr Developer
Operationalize ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale. AWS , Azure AI Foundry etc.
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL
• Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul APIs, Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
• Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics
• Model Performance Management: model monitoring, model validation, bias detection, explainability, performance, drift, outliers etc.
• Model Deployment: Thorough Understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline etc. model and data versioning
• Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark
• Statistics and Computing: Preferred knowledge of: AI ML Mathematics
Operationalize ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale. AWS , Azure AI Foundry etc.
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL
• Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul APIs, Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
• Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics
• Model Performance Management: model monitoring, model validation, bias detection, explainability, performance, drift, outliers etc.
• Model Deployment: Thorough Understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline etc. model and data versioning
• Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark
• Statistics and Computing: Preferred knowledge of: AI ML Mathematics
