Ai Ml Engineer

AI/ML Engineer (Remote)

What you'll need:

• Hands-on experience building end-to-end ML pipelines, with solid skills in data prep, feature engineering, and core data engineering
• Strong Python coding, using libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch
• Practical work with AWS data tools such as S3, Glue, Athena, and Spark for processing large datasets
• Deep knowledge of AWS ML services, mainly SageMaker, SageMaker Pipelines, Feature Store, and model deployment
• A track record of building ML models using supervised and unsupervised methods, predictive analytics, and statistical techniques
• Background in SAS for analytics and ML, including building, validating, and deploying models in large enterprise settings
• MLOps know-how covering the full model lifecycle: CI/CD, monitoring, retraining, and supporting models in production
• Ability to own technical design through build, deployment, and delivery with little supervision

Good to have:

• Experience creating reusable ML platforms, feature stores, and company-wide AI/ML solutions
• Familiarity with model explainability, Responsible AI, observability, and ML governance
• Knowledge of real-time streaming and event-driven setups using Kafka, Kinesis, Lambda, and Step Functions
• Experience with cloud-native APIs, microservices, and connecting enterprise apps on AWS