ML Engineer with AI Deployment Experience

Job Title: Machine Learning Engineer – AI Deployments on AWS Cloud Location: Remote (Authorized to work in USA only) Contract - W2/C2C/1099 Rate: $70/hr to $75/hr Overview: We are seeking a Machine Learning Engineer experienced in developing, deploying, and optimizing AI/ML solutions using AWS Cloud. The ideal candidate will have end-to-end ownership of the ML lifecycle — from data ingestion and model training to scalable deployment, monitoring, and continuous improvement using AWS-native services. Key Responsibilities: Model Development & Training • Design, develop, and optimize machine learning and deep learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn. • Perform data preprocessing, feature engineering, and model evaluation using AWS data and analytics services. • Collaborate with data scientists to productionize research models into scalable, reliable cloud-based AI solutions. AWS Cloud AI Deployments • Deploy and manage ML models in AWS SageMaker (training jobs, endpoints, pipelines, and model registry). • Build serverless inference APIs using AWS Lambda, API Gateway, or ECS/Fargate. • Implement real-time or batch inference pipelines with AWS Step Functions, Kinesis, or AWS Batch. • Manage containerized workloads for ML inference using Docker and Amazon EKS (Kubernetes). MLOps & Automation • Develop CI/CD pipelines for ML using AWS CodePipeline, CodeBuild, and CodeCommit (or GitHub Actions). • Automate data versioning, model versioning, and model retraining using SageMaker Pipelines, MLflow, or DVC. • Monitor model performance, data drift, and prediction accuracy using Amazon CloudWatch, AWS Model Monitor, or Evidently AI. Data Engineering Collaboration • Work closely with data engineers to design scalable data ingestion and transformation pipelines using: • AWS Glue, AWS DataBrew, AWS Data Pipeline, or Apache Spark on EMR. • Ensure data lineage, quality, and compliance within AWS data lakes and Redshift environments. Optimization & Scaling • Optimize model performance, latency, and cost efficiency using AWS Inferentia, Elastic Inference, and Auto Scaling. • Leverage GPU/TPU-based instances for high-performance training and fine-tuning tasks.