Must Have Technical/Functional Skills
- Strong proficiency in Python for AI/ML development.
- Hands-on experience with OpenAI and modern AI frameworks.
- Experience with AWS services for AI deployment.
- Proven leadership and ability to guide engineering teams
Good to have
- Solid understanding of ETL processes and data integration.
- Airflow and Harness for managing AI pipelines.
- Familiarity with platforms for data management.
- Familiarity with automated testing and deployment pipelines.
Roles & Responsibilities
- Build and maintain AI/ML pipelines on AWS, leveraging services such as Amazon S3 for data storage, AWS Lambda for
serverless functions, and Amazon EC2 for compute resources.
- Deploy trained models into production environments using AWS SageMaker Endpoints, AWS Lambda, or containerization technologies like Docker and Kubernetes on AWS EKS.
- Implement MLOps practices for continuous integration, continuous delivery (CI/CD), and monitoring of ML models.
- Preprocess and analyze data, engineer features, and select appropriate algorithms for specific problems.
- Utilize AWS services like Amazon SageMaker for efficient model training, hyperparameter tuning, and experiment
tracking.
- Analyzes data, builds models, and uncovers insights, often using Python for exploration and prototyping.
Salary Range- $110,000-$135,000 a year
