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Title: Machine Learning Engineer (AWS + MLOps + LLMs) Location: Remote / Hybrid About the Role LowPropTax is looking for a Machine Learning Engineer who can manage the full ML lifecycle — from data preparation and model experimentation to cloud deployment and monitoring. You will build and scale ML systems using AWS tools, with a focus on Lambda, SageMaker, and Databricks. Responsibilities Design and automate end-to-end ML pipelines using AWS Lambda, SageMaker, and Step Functions. Experiment with models including LLMs, regression, and classification tasks. Use Databricks for feature engineering, model training, and performance tuning. Deploy models to production environments with CI/CD automation. Implement monitoring for accuracy, drift, and cost optimization. Collaborate with data and backend teams to integrate ML systems with core business applications. Document experiments, version models, and manage reproducibility. Requirements Strong Python programming experience (Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow). Proven experience writing and deploying AWS Lambda functions. Experience with SageMaker, EC2, S3, IAM, and CloudWatch. Solid understanding of data workflows using Databricks or Spark. Familiarity with LLMs and prompt-based model fine-tuning. Knowledge of CI/CD, Git, and Docker. Good grasp of statistics, model evaluation, and performance metrics. Preferred Skills Experience building APIs for model inference. Understanding of MLflow, DVC, or similar model tracking tools. Hands-on experience with cost optimization in AWS ML workloads. Familiarity with data governance and versioning best practices. Ideal Profile This role blends data science experimentation with MLOps deployment. You’ll work on both — building models and running them in production. Compensation: Competitive, based on experience. Would you like me to make a shorter LinkedIn version (2-3 lines per section, punchier tone for posts and job boards)?