AWS ML Developer - Python, Azure Cloud

Job Description

Required Skills:

· Python

· Amazon Web Service (AWS) Cloud Computing

· Azure Machine Learning (ML)/Generative AI

Essential

Skills:

· Design and implement ML pipelines using AWS Sage Maker, including data preprocessing, model training, tuning, and deployment.

· Develop and integrate Generative AI applications using AWS Bedrock and foundation models (e.g., Titan, Claude, Llama).

· Build APIs and microservices to expose ML models for consumption by applications.

· Optimize ML workflows for cost efficiency and scalability in AWS environments.

· Collaborate with data scientists and business stakeholders to translate requirements into technical solutions.

· Implement security best practices for ML models and data in AWS.

· Monitor and maintain deployed models, ensuring performance and reliability.

· Hands-on experience with AWS Sage Maker (training, inference, pipelines, model registry).

· Strong knowledge of AWS Bedrock and generative AI concepts (LLMs, prompt engineering).

· Proficiency in Python and ML frameworks (Tensor Flow, PyTorch, Scikit-learn).

· Experience with AWS services Lambda, API Gateway, S3, IAM, Cloud Watch.

· Familiarity with MLOps practices and CI/CD pipelines for ML.

· Understanding of data engineering concepts and feature engineering.

· Excellent problem-solving and communication skills.

Experience: 6-8 years

Requirements

Required Skills:

• Python
• Amazon Web Service (AWS) Cloud Computing
• Azure Machine Learning (ML)/Generative AI Essential

Skills:

• Design and implement ML pipelines using AWS Sage Maker, including data preprocessing, model training, tuning, and deployment.
• Develop and integrate Generative AI applications using AWS Bedrock and foundation models (e.g., Titan, Claude, Llama).
• Build APIs and microservices to expose ML models for consumption by applications.
• Optimize ML workflows for cost efficiency and scalability in AWS environments.
• Collaborate with data scientists and business stakeholders to translate requirements into technical solutions.
• Implement security best practices for ML models and data in AWS.
• Monitor and maintain deployed models, ensuring performance and reliability.
• Hands-on experience with AWS Sage Maker (training, inference, pipelines, model registry).
• Strong knowledge of AWS Bedrock and generative AI concepts (LLMs, prompt engineering).
• Proficiency in Python and ML frameworks (Tensor Flow, PyTorch, Scikit-learn).
• Experience with AWS services Lambda, API Gateway, S3, IAM, Cloud Watch.
• Familiarity with MLOps practices and CI/CD pipelines for ML.
• Understanding of data engineering concepts and feature engineering.
• Excellent problem-solving and communication skills.

Experience:

6-8 years