Senior AI Engineer: End-to-End ML & Production Systems

EPAM Systems; México; Remote; MXN 450,000 - 750,000; Full time

Job summary

EPAM Systems is seeking a Senior AI Engineer to own the end-to-end lifecycle of ML systems, from problem formulation to production deployment. You will design scalable ML architectures, build robust pipelines, and collaborate with cross-functional teams to deliver impactful AI-driven solutions.

Role emphasizes end-to-end ML lifecycle, data processing for batch and real-time systems, model deployment, monitoring, and cost-efficient operations.

Qualifications

• 3+ years of experience in machine learning engineering or a related field.
• Expertise in MLOps practices for managing the ML lifecycle.
• Proficiency in PySpark and Python for building scalable data and ML pipelines.
• Background in designing and deploying production-grade ML systems.
• Understanding of batch and real-time data processing architectures.

Responsibilities

• Own end-to-end lifecycle of ML systems: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring.
• Design and implement scalable ML architectures including batch and real-time pipelines.
• Work with large-scale structured and unstructured datasets (e.g., clickstream, transactions, text, images).
• Develop and deploy models such as recommender, ranking, forecasting, and classification/regression systems.
• Apply deep learning techniques including neural networks, transformers, and representation learning where appropriate.

Skills

ML engineering

MLOps

Problem formulation

Cross-functional collaboration

Tools

PySpark

Python

Kafka

MLflow

PyTorch

Job description

EPAM Systems is seeking a Senior AI Engineer to own the end-to-end lifecycle of ML systems, from problem formulation to production deployment. You will design scalable ML architectures, build robust pipelines, and collaborate with cross-functional teams to deliver impactful AI-driven solutions.

Role emphasizes end-to-end ML lifecycle, data processing for batch and real-time systems, model deployment, monitoring, and cost-efficient operations.