AWS ML Developer Python, Azure
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Job Location:
Monthly Salary:
Experience Required:
Posted on:
7 hours ago
Vacancies:
1 Vacancy
Job Summary
Required Skills:
Python
Amazon Web Service (AWS) Cloud Computing
Azure Machine Learning (ML)/Generative AI
Essential Skills:
Design and implement ML pipelines using AWS SageMaker 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 SageMaker (training inference pipelines model registry).
Strong knowledge of AWS Bedrock and generative AI concepts (LLMs prompt engineering).
Proficiency in Python and ML frameworks (TensorFlow PyTorch Scikit-learn).
Experience with AWS services Lambda API Gateway S3 IAM CloudWatch.
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
Required Skills:
Required Skills: Python Amazon Web Service (AWS) Cloud Computing Azure Machine Learning (ML)/Generative AI Essential Skills: Design and implement ML pipelines using AWS SageMaker 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 SageMaker (training inference pipelines model registry). Strong knowledge of AWS Bedrock and generative AI concepts (LLMs prompt engineering). Proficiency in Python and ML frameworks (TensorFlow PyTorch Scikit-learn). Experience with AWS services Lambda API Gateway S3 IAM CloudWatch. 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
Company Industry
IT Services and IT Consulting
Key Skills
- APIs
- Docker
- Jenkins
- REST
- Python
- AWS
- NoSQL
- MySQL
- JavaScript
- Postgresql
- Django
- GIT
