Senior ML Infrastructure Engineer

Machine Learning Engineering

Location-specific conditions & benefits*

Argentina

We are seeking a Senior ML Infrastructure Engineer to bolster our MLOps team, overseeing the development and maintenance of our enterprise machine learning platform while driving innovation in scalable ML infrastructure and deployment practices.

Responsibilities

Provide expert guidance on ML technologies, tools, and MLOps best practices focused on model observability, tracking, and deployment
Build and maintain robust batch processing and ML inference pipelines to enable efficient model execution
Automate ML model deployment processes with CI/CD pipelines to streamline production workflows
Monitor the health, performance, reliability, and scalability of deployed models and infrastructure
Integrate ML inference services seamlessly with other applications or systems
Enable scalable, high-performance deployments of ML models that perform well under production load
Collaborate directly with client stakeholders and team members to ensure requirements are met and tasks are completed effectively
Implement infrastructure solutions that support data processing pipelines and batch inferencing
Create comprehensive unit tests for ML deployment, inference, and post-processing methods
Maintain clear and proactive communication with team members and stakeholders to ensure alignment

Requirements

3+ years of experience with AWS services and MLOps-related infrastructure, focusing on scalable ML model deployment
Expertise in infrastructure-as-code tools, enabling efficient and consistent infrastructure setup
Strong background in setting up and monitoring infrastructure for data pipelines and ML inference pipelines
Demonstrated task ownership abilities, with experience working directly with client stakeholders and cross-functional teams
Skills in writing unit tests for ML deployment, inference, and related methods to ensure code reliability
Clear and effective communication skills with the ability to seek clarifications when needed

Nice to have

Experience with Google Cloud Platform (GCP) and its ML-related services
Competency in working with Snowflake as a data platform for ML workflows
Familiarity with Feature Store platforms to improve feature management
Background in using Spark and AWS Elastic MapReduce (EMR) for distributed data processing
Understanding of data curation best practices for ML model training and enabling high-quality datasets
Flexibility to participate in on-call rotations, ensuring 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