Senior AI Systems Engineer: Real-Time & Batch ML Delivery

EPAM Systems; México; Remote; MXN 900,000 - 1,500,000; Full time

Benefits offered by this job

Healthcare benefits

Upskilling & certification courses

Paid time off & sick leave

LinkedIn Learning access

Job summary

EPAM Systems seeks a Lead AI Engineer to drive end-to-end ML delivery, from problem framing to deployment and monitoring. You will design scalable batch and real-time ML architectures and ship production-grade models across multiple use cases, partnering with engineering, product, and business stakeholders to define KPIs and outcomes.

The role requires 5+ years of production ML experience, expertise in scalable architectures, and strong data handling across clickstream, transactional, text, and

Qualifications

• 5+ years of experience building and deploying ML systems in production.
• Experience designing scalable ML architectures for batch and real-time pipelines.
• Strong ability to work with large-scale structured and unstructured datasets (clickstream, transactional, text, image).
• Experience with DL techniques including neural networks and transformers.

Responsibilities

• Lead the full ML system lifecycle: problem framing, data collection, feature engineering, model training, evaluation, deployment, and monitoring.
• Design scalable ML architectures for batch and real-time data.
• Build and deploy models across multiple use cases (recommendation, ranking, forecasting, classification, regression).
• Create and maintain feature stores, model registries, and experiment tracking systems.
• Collaborate with engineering, product, and business teams to define KPIs and success metrics.

Skills

ML systems

Batch & real-time pipelines

KPIs & outcomes

Large-scale data handling

Tools

Feature stores

Model registries

Experiment tracking

Deep learning frameworks

Job description

EPAM Systems seeks a Lead AI Engineer to drive end-to-end ML delivery, from problem framing to deployment and monitoring. You will design scalable batch and real-time ML architectures and ship production-grade models across multiple use cases, partnering with engineering, product, and business stakeholders to define KPIs and outcomes.

The role requires 5+ years of production ML experience, expertise in scalable architectures, and strong data handling across clickstream, transactional, text, and