Senior ML Engineer: Scale & Deploy AI Models (Poland)

GlobalLogic; Town of Poland (NY); On-site; USD 48,000 - 70,000; Full time

Benefits offered by this job

Relocation options

Rotation programs

Comprehensive benefits

Job summary

GlobalLogic is seeking a Machine Learning Engineer to deploy and maintain models developed by data scientists in production environments. You will handle infrastructure, scaling, and performance optimization while collaborating with data scientists and engineers to productionize models.

You will work with cloud-native tools on GCP (Vertex, BigQuery) and containerized pipelines, enabling scalable experimentation and ML deployment across teams globally.

Qualifications

• Strong understanding or hands-on ML experience.
• Python (must be very strong).
• SQL proficiency.
• Terraform expertise.
• GCP experience (Vertex, BigQuery, Model Registry).
• Docker proficiency.
• FastAPI or similar for APIs.
• Ray for distributed processing.
• Airflow/GCP Composer for pipelines.
• Package management (poetry, etc.).
• Scalable experimentation & model tracking.
• Scalable ML deployment experience.

Responsibilities

• Deploy and maintain ML algorithms developed by data scientists.
• Provide infrastructure, scaling, performance optimization, and maintenance in production.
• Model deployment: turn models into live production services.
• Performance optimization for latency across CPUs/GPUs.
• Design, build, and maintain components to train, deploy, and scale models.
• Implement logging/monitoring and fix bugs in collaboration with teams.
• Collaborate with data scientists and engineers to productionize models.
• Experimentation: enable scalable experiments and evaluation.
• Support DS team with cloud tech (GCP, Vertex, Docker) for lifecycle efficiency.

Skills

Python

SQL

GCP

Terraform

Docker

FastAPI

Ray

Airflow

MLFlow

PyTorch

Model deployment

Tools

Docker

BigQuery

Vertex AI

Job description

GlobalLogic is seeking a Machine Learning Engineer to deploy and maintain models developed by data scientists in production environments. You will handle infrastructure, scaling, and performance optimization while collaborating with data scientists and engineers to productionize models.

You will work with cloud-native tools on GCP (Vertex, BigQuery) and containerized pipelines, enabling scalable experimentation and ML deployment across teams globally.