Artificial Intelligence Engineer

Role & responsibilities

1. ML Infrastructure and Operations:

• Design and implement ML infrastructure at scale, leveraging cloud-based tools and services.
• Set up and manage MLOps pipelines to streamline model development, deployment, monitoring, and management processes.

2. Application and Tool Development:

• Build and provision the necessary applications and environments required by Data Scientists to support their work.
• Develop and maintain data pipelines for both relational and NoSQL databases, ensuring efficient data flow and storage.

3. Cloud and DevOps Expertise:

• Utilize AWS services such as Sagemaker, Lambda, S3, Bedrock, Model Monitoring, and AWS Gateway for ML operations.
• Implement CI/CD pipelines to automate model deployment and improve overall efficiency.

4. Collaboration and Support:

• Work closely with Data Scientists to ensure the necessary infrastructure and tools are available for data analysis, model training, and execution.
• Provide ongoing support for model monitoring and management in a production environment.

5. Continuous Improvement:

• Stay updated with advancements in ML technologies, MLOps practices, and cloud-based solutions.
• Optimize existing processes to improve scalability, reliability, and performance.

Preferred candidate profile

• Minimum of 5+ years of experience in data engineering and analytics.
• At least 2+ years of experience in setting up ML infrastructure and MLOps processes in AWS cloud environments
• Proficiency in Object-Oriented Programming (Python, Java).
• Expertise in Pythons ML extensions (pandas, numpy, scikit-learn, etc.).
• Strong experience with AWS services such as Sagemaker, Lambda, S3, Bedrock, Model Monitoring, and AWS Gateway.
• Experience in building data pipelines on relational and NoSQL databases.
• Familiarity with CI/CD tools.
• Strong knowledge of ML algorithms, infrastructure design, and cloud-based solutions.