Senior AI Solution Engineer with Databricks, ML

EPAM Systems; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

EPAM Systems Inc. seeks a Senior AI Solution Engineer to design, deploy, and operate production-grade ML systems. You will build streaming pipelines, enable real-time feature generation, and ensure reliability and scalability of ML solutions in production environments.

The role combines hands-on data engineering and machine learning engineering to deliver real-time and batch ML solutions. You will work with Databricks, Spark Streaming, and AWS, processing large-scale structured and unstructured

Qualifications

• 5+ years building feature pipelines for offline and online workloads.
• Proficiency in Databricks, Spark/PySpark, Python, and SQL.
• Experience with real-time training and inference pipelines.
• Experience handling large-scale unstructured data.
• Knowledge of Spark Streaming.
• Experience with real-time feature generation and low-latency inference.
• Proven track record of production-grade ML systems.
• English proficiency at B2 level or higher.
• Nice to have MLflow and MLOps familiarity.
• Experience with AWS.
• Background in Customer Analytics, Recommendation Systems, or Retail.

Responsibilities

• Design and optimize feature engineering pipelines for 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.
• Support batch pipelines for model training and retraining.
• Operate and maintain production ML systems to ensure consistent performance.

Job description

We are seeking a Senior AI Solution Engineer with expertise in Databricks and Machine Learning to design, deploy, and operate production‑grade ML systems. The ideal candidate is a hands‑on Data Engineer / Machine Learning Engineer with a proven track record of delivering real‑time and batch ML solutions, strong capability to build streaming pipelines, and experience ensuring the reliability and scalability of ML systems in production environments.

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
• Support batch pipelines for model training and retraining
• Operate and maintain production ML systems to ensure consistent performance

Requirements

• 5+ years of experience building, deploying, and operating offline and online feature engineering pipelines
• Proficiency in Databricks, Spark/PySpark, Python, and SQL
• Expertise in model training and real‑time inferencing pipelines
• Experience processing large-scale unstructured data
• Knowledge of Spark Streaming
• Experience with real‑time feature generation and low‑latency model inference
• Proven track record of running production‑grade ML systems
• English proficiency at B2 level or higher
• Nice to have Familiarity with MLflow and MLOps practices
• Experience with AWS
• Background in Customer Analytics, Recommendation Systems, or Retail