AI/ML Data with AWS

Key Responsibilities

Design, develop, train, and deploy machine learning and deep learning models for real-world business use cases

Build end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and deployment

Implement and manage ML workflows using AWS services such as SageMaker, S3, EC2, Lambda, Glue, and Step Functions

Deploy models as scalable APIs using Docker, REST endpoints, and CI/CD pipelines

Monitor model performance, drift, and retraining strategies in production

Collaborate with data engineering teams to ensure high-quality, reliable data pipelines

Optimize model performance, cost, and scalability on AWS infrastructure

Ensure solutions meet security, compliance, and governance standards

Required Skills & Experience

3+ years of experience in Machine Learning / AI development

Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)

Hands-on Experience With AWS ML Stack, Including

Amazon SageMaker (training, endpoints, pipelines)

S3, EC2, IAM, Lambda

Glue, Athena, Redshift (nice to have)

Experience with model deployment and MLOps practices

Solid understanding of supervised, unsupervised, and deep learning techniques

Experience with REST APIs, Docker, and Git-based version control

Strong problem-solving and communication skills