Senior AI Engineer with Databricks

EPAM Systems; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

EPAM Systems Inc is seeking a Senior AI Engineer with Databricks to design, deploy, and operate production-grade machine learning systems. The role focuses on feature engineering, model training, and real-time inference pipelines to support scalable analytics and decisioning.

The candidate will design feature pipelines, build end-to-end ML workflows, and ensure reliability of streaming and batch processes in production environments. Strong English skills required.

Qualifications

• 5+ years building, deploying, and operating feature engineering pipelines.
• Strong experience with model training and real-time inferencing pipelines.
• Hands-on Databricks, Spark/PySpark, Python and SQL expertise.
• Experience processing large-scale unstructured data and streaming.
• Knowledge of Spark Streaming and real-time feature generation.
• Proven track record delivering production-grade ML systems.
• English proficiency at B2 level or higher.
• Nice-to-have MLflow and MLOps familiarity; customer analytics or retail a plus.

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 with a track record of delivery.

Skills

Feature engineering

Model training

Real-time inference

Data processing at scale

Spark Streaming

Python

SQL

MLops

English (B2+)

Tools

Databricks

Spark/PySpark

MLflow

AWS

Job description

We are seeking a Senior AI Engineer with Databricks to design, deploy, and operate production-grade machine learning systems, with a strong focus on feature engineering, model training, and real-time inference pipelines.

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 with a strong track record of delivery

Requirements

• 5+ 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
• Proven track record of running production‑grade ML systems
• English proficiency at B2 level or higher
• Nice to have Experience with MLflow and MLOps practices Familiarity with AWS Background in Customer Analytics, Recommendation Systems, or Retail