Full Job Description
We are looking for a hands-on AI/Machine Learning Engineer with proven experience deploying and running optimized feature engineering (offline and online), model training, and inference pipelines. Experience with unstructured data processing and Spark Streaming is a must. Real-time feature generation and inference are required for this project, along with batch pipelines for model training. Responsibilities Design and optimize feature engineering pipelines for both batch and real-time workloads Build, deploy, and maintain scalable model training and inference pipelines Process structured and unstructured data at scale Develop streaming solutions using Spark Streaming Enable real-time feature generation and model serving Ensure reliability, scalability, and performance of ML solutions in production Requirements 3+ years of experience building, deploying, and operating offline and online feature engineering pipelines Strong experience with model training and real-time inferencing pipelines Hands-on expertise in Databricks, Spark/PySpark, Python, and SQL Experience processing large-scale unstructured data Strong knowledge of Spark Streaming Experience with real-time feature generation and low-latency model inference Experience building and supporting batch pipelines for model training and retraining Proven track record of running production-grade ML systems English proficiency at B2 level or higher Nice to have Experience with MLflow and MLOps Experience with AWS Background in Customer Analytics, Recommendation Systems, or Retail
