Senior AI Engineer: Build Scalable ML Pipelines & Systems

EPAM Systems; Colombia; Remote; COP 120,000,000 - 180,000,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off and sick leave

Upskilling and certification

Job summary

EPAM Systems is seeking a Senior AI Engineer to own end-to-end ML systems lifecycle, from problem framing to production deployment, across scalable architectures and large-scale datasets. You will build feature stores, model registries, and experiments, ensuring latency, reliability, and cost efficiency while collaborating with engineering and business teams.

The role emphasizes deep learning techniques, real-time and batch processing, and proficiency in PyTorch and MLOps within diverse,

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.
• Experience working with large-scale structured and unstructured datasets.
• English proficiency at B2 level or higher.

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 such as clickstream, transactional, text, and image data.
• Develop and deploy models such as recommendation systems, ranking models, forecasting models, and classification/regression systems.
• Apply deep learning techniques including neural networks, transformers, and representation learning where appropriate.
• Build and maintain feature stores, model registries, and experiment tracking systems.
• Ensure production readiness through latency optimization, scalability, reliability, and cost efficiency.
• Collaborate with engineering, product, and business teams to define KPIs and success metrics.

Skills

Machine learning engineering

PySpark

Python

MLOps

Kafka

MLflow

PyTorch

Deep learning

Tools

PySpark

Python

Apache Kafka

MLflow

PyTorch

Job description

EPAM Systems is seeking a Senior AI Engineer to own end-to-end ML systems lifecycle, from problem framing to production deployment, across scalable architectures and large-scale datasets. You will build feature stores, model registries, and experiments, ensuring latency, reliability, and cost efficiency while collaborating with engineering and business teams.

The role emphasizes deep learning techniques, real-time and batch processing, and proficiency in PyTorch and MLOps within diverse,