Lead AI Engineer

EPAM Systems; Argentina; Presencial; ARS 2.790.000 - 5.022.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Healthcare benefits

Global career opportunities

Paid time off & sick leave

Upskilling & certification courses

Descripción de la vacante

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

You will lead the ML lifecycle, work with large-scale data, and build models such as recommender systems, forecasting models, and

Formación

• 5+ years of experience building and deploying ML systems in production environments.
• Experience designing scalable batch and real-time ML pipelines.
• Experience with large-scale structured and unstructured data (clickstream, transactions, text, images).
• Proven ability to develop recommendation, ranking, forecasting, and classification/regression models.
• Deep learning expertise with neural networks, transformers, and representation learning.
• Experience with feature stores, model registries, and experiment tracking.
• Strong collaboration to define KPIs and outcomes.
• English proficiency at B2 (Upper-Intermediate) level or higher.

Responsabilidades

• Lead the full ML system lifecycle from problem formulation to monitoring.
• Design scalable ML architectures for batch and real-time pipelines.
• Work with large-scale datasets including clickstream, transactional, text, and image data.
• Build and deploy models such as recommendation systems, ranking models, forecasting models, and classification/regression systems.
• Apply deep learning methods including neural networks, transformers, and representation learning.
• Create and maintain feature stores, model registries, and experiment tracking systems.
• Ensure production readiness by optimizing latency, scalability, reliability, and cost efficiency.
• Partner with engineering, product, and business teams to define KPIs and success metrics.

Conocimientos

Production ML systems

Scalable architectures

Large-scale data handling

Recommendations & forecasting

Deep learning

Experiment tracking

KPIs & metrics

English proficiency (B2+)

Herramientas

Feature stores

Model registries

Experiment tracking systems

Latency optimization tools

Descripción del empleo

We are looking for a Lead AI Engineer to drive end-to-end ML delivery, from problem framing and data sourcing to deployment and monitoring. You will design scalable batch and real-time ML architectures, ship production-grade models across multiple use cases, and partner with engineering, product, and business stakeholders to define KPIs and outcomes.

Responsibilities

• Lead the full ML system lifecycle: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring
• Design and implement scalable ML architectures that support batch and real-time pipelines
• Handle large-scale structured and unstructured datasets including clickstream, transactional, text, and image data
• Build and ship models such as recommendation systems, ranking models, forecasting models, and classification/regression systems
• Apply deep learning methods including neural networks, transformers, and representation learning when appropriate
• Create and maintain feature stores, model registries, and experiment tracking systems
• Ensure production readiness by optimizing latency, scalability, reliability, and cost efficiency
• Partner with engineering, product, and business teams to define KPIs and success metrics

Requirements

• 5+ years of experience building and deploying ML systems in production environments
• Hands-on expertise designing scalable ML architectures, including batch and real-time pipelines
• Strong ability to work with large-scale structured and unstructured datasets, including clickstream, transactional, text, and image data
• Demonstrated experience developing recommendation systems, ranking models, forecasting models, and classification/regression systems
• Deep understanding of deep learning techniques including neural networks, transformers, and representation learning
• Practical skills building and maintaining feature stores, model registries, and experiment tracking systems
• Solid knowledge of production readiness topics such as latency optimization, scalability, reliability, and cost efficiency
• Proven collaboration skills with engineering, product, and business teams to define KPIs and success metrics
• English proficiency at B2 (Upper-Intermediate) level or higher

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn