Must have skills:
- 5+ years of relevant hands-on technical experience mathematical optimization techniques and implementing and developing cloud ML solutions on AWS.
- Experience in designing and implementing mathematical optimization models using Gurobi to address business challenges such as workforce scheduling, supply chain optimization, and resource allocation.
- Experience in utilizing combinatorial optimization techniques like branch and cut, branch and bound, and metaheuristics (e.g., genetic algorithms, simulated annealing) to solve discrete optimization problems.
- Must be able to analyze complex business problems and identify opportunities for optimization, applying combinatorial optimization methods to discrete problems.
- Experience in monitoring and analyzing the performance of optimization models, identifying areas for improvement.
- Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
- Implement and manage MLOps principles and best practices for ML architecture.
- Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
- Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
Good to have skills:
- Experience in dynamic pricing, price promotion and price optimization.
- Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
- Experience with software development.
- Able to effectively design software architecture as required.