Senior AI Engineer

EPAM Systems; Argentina; In loco; ARS 1.200.000 - 1.800.000; Tempo pieno

Vantaggi offerti da questo lavoro

Healthcare benefits

Global teams

Paid time off

Upskilling & certification courses

LinkedIn Learning access

Volunteer opportunities

Descrizione del lavoro

EPAM Systems seeks a Senior AI Engineer to own the end-to-end lifecycle of ML systems, from problem formulation to deployment and monitoring. You will design scalable architectures and production-grade models across diverse use cases while collaborating with cross-functional teams.

The role emphasizes building pipelines, feature stores, and experiment tracking, with a strong focus on latency, reliability, and cost efficiency in production environments.

Competenze

• 3+ years of experience building and deploying ML systems in production environments.
• Expertise in designing scalable ML architectures with batch and real-time pipelines.
• Proficiency with large-scale structured and unstructured datasets (clickstream, transactional, text, image).
• Experience developing recommendation, ranking, forecasting, and classification/regression models.
• Knowledge of deep learning techniques (neural networks, transformers, representation learning).
• Experience building feature stores, model registries, and experiment tracking systems.
• Understanding of production readiness: latency, scalability, reliability, cost efficiency.
• Ability to collaborate with engineering, product, and business to define KPIs and metrics.
• English proficiency at B2 level or higher.

Mansioni

• Own end-to-end lifecycle of ML systems: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring.
• Design scalable ML architectures including batch and real-time pipelines.
• Work with large-scale structured and unstructured datasets (clickstream, transactional, text, image).
• Develop and deploy models such as recommendations, ranking, forecasting, and classification/regression systems.
• Apply deep learning techniques including neural networks, transformers, and representation learning.
• Build and maintain feature stores, model registries, and experiment tracking systems.
• Ensure production readiness: latency optimization, scalability, reliability, and cost efficiency.
• Collaborate with engineering, product, and business teams to define KPIs and success metrics.

Conoscenze

ML system design

Real-time data pipelines

Large-scale data processing

Deep learning techniques

Cross-functional collaboration

Strumenti

Feature stores

Model registries

Experiment tracking

Descrizione del lavoro

We are seeking a Senior AI Engineer to own the end-to-end lifecycle of ML systems, from problem formulation and data collection through model training, deployment, and monitoring. This role involves designing scalable architectures and deploying production-grade models across a variety of use cases while collaborating closely with cross-functional teams.

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

Requirements

• 3+ years of experience in building and deploying ML systems in production environments


• Expertise in designing scalable ML architectures, including batch and real-time pipelines


• Proficiency in working with large-scale structured and unstructured datasets, including clickstream, transactional, text, and image data


• Experience developing recommendation systems, ranking models, forecasting models, and classification/regression systems


• Knowledge of deep learning techniques including neural networks, transformers, and representation learning


• Skills in building and maintaining feature stores, model registries, and experiment tracking systems


• Understanding of production readiness considerations such as latency optimization, scalability, reliability, and cost efficiency


• Capability to collaborate with engineering, product, and business teams to define KPIs and success metrics


• English proficiency at B2 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