Build, orchestrate, and monitor model pipelines including feature engineering, inferencing, and continuous model training
Scaling machine learning algorithms to work on massive data sets and strict SLAs
Build & Enhance ML Engineering platforms and components
Implement ML Ops including model KPI measurements, tracking, data, and model drift & model feedback loop
Write production-ready code that is easily testable, understood by other developers, and accounts for edge cases and errors
Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews
Collaborate with client teams and global development teams to successfully deliver projects
Uses bug tracking, code review, version control, and other tools to organize and deliver work
Participate in scrum calls, and effectively communicate work progress, issue,s and dependencies
Consistently contribute to researching & evaluating the latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts, and creating prototype solutions.
Requirements & Skills:
Bachelor’s/Master’s degree with specialization in Computer Science, MIS, IT, or another computer-related discipline
2-4 years of experience in deploying and productionizing ML models
Strong programming expertise in Python / PySpark
Experience in ML platforms like Dataiku, Sagemaker, MLFlow, or other platforms
Experience in deploying models to cloud services like AWS, Azure, GCP
Expertise in crafting ML Models for high performance and scalability
Experience in implementing feature engineering, inferencing pipelines, and real-time model predictions
Experience in ML Ops to measure and track model performance
Good fundamentals of machine learning and deep learning
Knowledgeable of core Computer Science concepts such as common data structures, algorithms, and design patterns
