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)?