Description

You will design and deliver ML architecture patterns for native and hybrid cloud environments, integrating AI/ML models into real-world systems.

Responsibilities

• Design and deliver ML architecture patterns operable in native and hybrid cloud environments.
• Research and select technical approaches to solve data integration and ML model training/deployment challenges.
• Identify emerging technologies and trends affecting the Data Science and ML life-cycle management.
• Develop best practices for ML life-cycle capabilities including data collection, feature engineering, MLOps, and model monitoring.

Required Skills

• 10+ years of experience implementing and deploying AI/ML solutions.
• Proven experience in Solution Architect, Software Architect, or Technical Lead roles.
• Deep expertise in ML models: Linear/Logistic Regression, SVM, Deep Neural Networks, Hidden Markov Models, and Topic Modeling.
• Hands-on experience with Python, R, scikit-learn, and Spark MLlib.
• In-depth experience with AWS AI/ML services, specifically Amazon SageMaker and API-driven services (Lex, Polly, Transcribe, Comprehend, Rekognition).
• Experience managing the ML life-cycle: data preparation, model management, deployment, and tuning.
• Strong ability to break down complex problems and evaluate multiple technical solutions.
• Experience working with open source software.

Preferred Skills

• Experience with Data Engineering project design and development on Data Platforms.

Key Skills

Education

Any Graduate