Senior AI Engineer

EPAM Systems; Argentina; Remote; ARS 1,800,000 - 3,000,000; Full time

Job summary

EPAM Systems is seeking a Senior AI Engineer to own the end-to-end lifecycle of machine learning systems, from problem formulation to production deployment. You will design scalable ML architectures, build robust pipelines, and collaborate across teams to deliver impactful AI-driven solutions.

Responsibilities include designing batch and real-time data pipelines, handling large-scale structured and unstructured data, and deploying models such as recommendations, forecasting, and classification

Qualifications

• 3+ years of experience in machine learning engineering or related field.
• Expertise in MLOps for managing the ML lifecycle.
• Proficiency in PySpark and Python for scalable pipelines.
• Experience designing and deploying production-grade ML systems.
• Understanding of batch and real-time data processing architectures.
• English proficiency at B2 level or higher.

Responsibilities

• Own end-to-end lifecycle of ML systems from problem formulation to deployment and monitoring.
• Design scalable ML architectures and robust data pipelines for large datasets.
• Develop and deploy models such as recommender, forecasting, and classification systems.
• Collaborate with engineering, product, and business teams to define KPIs and success metrics.

Skills

MLOps

PySpark

Python

Large-scale data processing

Production ML systems

English communication

Tools

Kafka

MLflow

PyTorch

Job description

We are looking for a Senior AI Engineer to own the end-to-end lifecycle of machine learning systems, from problem formulation to production deployment. You will design scalable ML architectures, build robust pipelines, and collaborate across teams to deliver impactful AI-driven solutions.ResponsibilitiesOwn end-to-end lifecycle of ML systems: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoringDesign and implement scalable ML architectures including batch and real-time pipelinesWork with large-scale structured and unstructured datasets such as clickstream, transactional, text, and image dataDevelop and deploy models such as recommendation systems, ranking models, forecasting models, and classification/regression systemsApply deep learning techniques including neural networks, transformers, and representation learning where appropriateBuild and maintain feature stores, model registries, and experiment tracking systemsEnsure production readiness through latency optimization, scalability, reliability, and cost efficiencyCollaborate with engineering, product, and business teams to define KPIs and success metricsRequirements3+ years of experience in machine learning engineering or a related fieldExpertise in MLOps practices for managing the ML lifecycleProficiency in PySpark and Python for building scalable data and ML pipelinesBackground in designing and deploying production-grade ML systemsUnderstanding of batch and real-time data processing architecturesExperience working with large-scale structured and unstructured datasetsEnglish proficiency at B2 level or higherNice to haveFamiliarity with Apache Kafka and MLflowKnowledge of Machine Learning and PyTorchWe 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