Lead ML Infrastructure Engineer

Machine Learning Engineering

Location-specific conditions & benefits*

Argentina

We are seeking a Lead ML Infrastructure Engineer to strengthen our MLOps team, focusing on the design and management of our enterprise machine learning platform while advancing scalable ML infrastructure and deployment practices.

Responsibilities

Provide expert advice on ML technologies, tools, and MLOps best practices with an emphasis on model observability, tracking, and deployment
Design and maintain robust batch processing and ML inference pipelines for efficient model execution
Automate ML model deployment processes through CI/CD pipelines to enhance production workflows
Monitor deployed models and infrastructure for health, performance, reliability, and scalability
Ensure seamless integration of ML inference services with other applications or systems
Enable deployments of ML models that scale efficiently and maintain high performance in production environments
Collaborate with client stakeholders and team members to ensure requirements are understood and tasks are completed effectively
Develop infrastructure solutions that support both data processing pipelines and batch inferencing capabilities
Write comprehensive unit tests to ensure reliability for ML deployment, inference, and post-processing methods
Maintain proactive and transparent communication with team members and stakeholders to ensure alignment

Requirements

5+ years of experience with AWS services and MLOps-focused infrastructure for scalable ML model deployment
Expertise in infrastructure-as-code tools, enabling efficient and consistent infrastructure provisioning
Strong background in setting up and monitoring infrastructure for data and ML inference pipelines
Demonstrated ability to take ownership of tasks and work collaboratively with client stakeholders and teams
Skills in writing effective unit tests for ML deployment, inference, and related methods
Proficiency in clear communication with the ability to ask for clarification when necessary

Nice to have

Knowledge of Google Cloud Platform (GCP) and its ML-specific services
Proficiency in using Snowflake as a data platform for ML workflows
Understanding of Feature Store platforms to enhance feature management processes
Background in Spark and AWS Elastic MapReduce (EMR) for processing distributed datasets
Familiarity with data curation best practices to support ML model training and high-quality dataset creation
Capability to participate in on-call rotations to maintain system reliability in production environments

Benefits

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