Machine Learning Engineer

Title:

Location:

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:

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