Lead AI Engineer

EPAM Systems; Argentina; Remote; ARS 136,660,000 - 197,397,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off and sick leave

LinkedIn Learning access

Global career opportunities

Volunteer opportunities

Job summary

EPAM Systems, Inc. is seeking 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 architectures and ship production-grade models across multiple use cases.

Collaborating with engineering, product and business teams, you will define KPIs, ensure production readiness, and advance feature stores, model registries, and experiment tracking. Strong DL methods and large-scale data experience are essential.

Qualifications

• 5+ years of experience building and deploying ML systems in production environments.
• Experience 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.

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.

Skills

ML systems

Scalable architecture

Large datasets

Deep learning

Recommendation systems

Model registries

Experiment tracking

Feature stores

Collaboration

KPIs

English proficiency

Tools

Feature stores

Model registries

Experiment tracking

Job description

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.ResponsibilitiesLead the full ML system lifecycle: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoringDesign and implement scalable ML architectures that support batch and real-time pipelinesHandle large-scale structured and unstructured datasets including clickstream, transactional, text, and image dataBuild and ship models such as recommendation systems, ranking models, forecasting models, and classification/regression systemsApply deep learning methods including neural networks, transformers, and representation learning when appropriateCreate and maintain feature stores, model registries, and experiment tracking systemsEnsure production readiness by optimizing latency, scalability, reliability, and cost efficiencyPartner with engineering, product, and business teams to define KPIs and success metricsRequirements5+ years of experience building and deploying ML systems in production environmentsHands-on expertise designing scalable ML architectures, including batch and real-time pipelinesStrong ability to work with large-scale structured and unstructured datasets, including clickstream, transactional, text, and image dataDemonstrated experience developing recommendation systems, ranking models, forecasting models, and classification/regression systemsDeep understanding of deep learning techniques including neural networks, transformers, and representation learningPractical skills building and maintaining feature stores, model registries, and experiment tracking systemsSolid knowledge of production readiness topics such as latency optimization, scalability, reliability, and cost efficiencyProven collaboration skills with engineering, product, and business teams to define KPIs and success metricsEnglish proficiency at B2 (Upper-Intermediate) level or higherWe offerInternational projects with top brandsWork with global teams of highly skilled, diverse peersHealthcare benefitsEmployee financial programsPaid time off and sick leaveUpskilling, reskilling and certification coursesUnlimited access to the LinkedIn Learning library and 22,000+ coursesGlobal career opportunitiesVolunteer and community involvement opportunitiesEPAM Employee GroupsAward-winning culture recognized by Glassdoor, Newsweek and LinkedIn