AI/ML Engineer/Developer (AWS, SageMaker, Predictive Analytics)

Job Summary:

We are seeking a talented and experienced AI/ML Engineer/Developer to join our dynamic team. This role involves designing, developing, and deploying cutting-edge machine learning and artificial intelligence solutions using AWS services, with a strong focus on SageMaker and predictive analytics. The ideal candidate will have a solid understanding of machine learning algorithms, deep learning architectures, and the ability to leverage AWS cloud services to build scalable and efficient AI/ML solutions.

Responsibilities:

1. Design, develop, and implement machine learning models and AI systems using AWS SageMaker and other AWS services.

2. Leverage SageMaker for end-to-end machine learning workflows, including data preprocessing, model training, tuning, and deployment.

3. Build and optimize predictive analytics models for various use cases, such as forecasting, anomaly detection, and recommendation systems.

4. Integrate machine learning models into existing applications and systems, ensuring seamless integration and optimal performance.

5. Develop and maintain CI/CD pipelines for machine learning models, ensuring efficient model deployment and monitoring.

6. Implement best practices for data governance, security, and privacy in AI/ML solutions.

7. Stay up-to-date with the latest advancements in machine learning, deep learning, and AI technologies, and contribute to the adoption of new techniques and tools.

Qualifications:

1. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Statistics, or a related field, or equivalent practical experience.

2. Minimum of 3 years of experience in machine learning engineering, with a strong focus on AWS services and SageMaker.

3. Proficient in programming languages such as Python, with experience in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). good

4. Solid understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning architectures, and reinforcement learning. good

5. Experience with predictive analytics techniques, such as time series forecasting, anomaly detection, and recommendation systems. good

6. Strong knowledge of AWS services for machine learning, including SageMaker, S3, EKS, and related services.

7. Familiarity with containerization technologies (e.g., Docker, Kubernetes) and their integration with AWS services.

8. Experience with data preprocessing, feature engineering, and data manipulation techniques.

9. Strong problem-solving and analytical skills, with the ability to break down complex problems into manageable components.

10. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

Behavioral Skills

1. Ability to build and maintain effective relationships with team members and cross-functional teams.

2. Collaborate effectively with other cross-platform team members and product teams to create efficient interfaces and styles.

3. Strong ability to visualize UI requirements and workflows.

4. Demonstrates initiative, thoroughness, and a strong sense of responsibility.

5. Exhibits good learning agility.