Synergy Global Technologies
Machine Learning Engineer
Synergy Global Technologies, Jersey City, New Jersey, United States, 07390
Role:
Machine Learning Engineer Location : New Jersey Looking for full time Role Position Overview Overall experience of minimum 7 years and machine learning experience of at least 3 - 4 years. : As a GCP ML Engineer, you'll design, develop, and maintain machine learning pipelines and infrastructure on the Google Cloud Platform (GCP). You’ll work closely with data scientists, engineers, and DevOps teams to ensure smooth integration and deployment of machine learning models. Key Responsibilities: Pipeline Development:
Build and automate end-to-end machine learning pipelines from data ingestion to model deployment. Infrastructure Management:
Develop and manage infrastructure for scalable machine learning solutions using GCP services such as AI Platform, Cloud Functions, BigQuery, and Kubernetes. CI/CD for ML Models:
Implement CI/CD processes for machine learning models, ensuring reliable and scalable deployment practices. Monitoring & Optimization:
Monitor and optimize machine learning models in production, ensuring high performance and uptime. Collaboration:
Work with cross-functional teams, including data engineers, software developers, and product teams, to ensure the successful deployment and operation of models. Technical Requirements: Experience with
Google Cloud Platform (GCP) , including GKE, AI Platform, Dataflow, and BigQuery services. Proficiency in
Python
and frameworks like
TensorFlow ,
PyTorch , or
Scikit-learn . Knowledge of
Kubernetes
and containerization (Docker). Experience with
CI/CD tools
such as Jenkins, CircleCI, or GitLab for ML pipelines. Strong knowledge of
DevOps
principles and tools (Terraform, Ansible). Preferred Qualifications: Hands-on experience with
MLFlow
or
Kubeflow . Familiarity with
data engineering
processes, ETL pipelines, and data lakes.
Machine Learning Engineer Location : New Jersey Looking for full time Role Position Overview Overall experience of minimum 7 years and machine learning experience of at least 3 - 4 years. : As a GCP ML Engineer, you'll design, develop, and maintain machine learning pipelines and infrastructure on the Google Cloud Platform (GCP). You’ll work closely with data scientists, engineers, and DevOps teams to ensure smooth integration and deployment of machine learning models. Key Responsibilities: Pipeline Development:
Build and automate end-to-end machine learning pipelines from data ingestion to model deployment. Infrastructure Management:
Develop and manage infrastructure for scalable machine learning solutions using GCP services such as AI Platform, Cloud Functions, BigQuery, and Kubernetes. CI/CD for ML Models:
Implement CI/CD processes for machine learning models, ensuring reliable and scalable deployment practices. Monitoring & Optimization:
Monitor and optimize machine learning models in production, ensuring high performance and uptime. Collaboration:
Work with cross-functional teams, including data engineers, software developers, and product teams, to ensure the successful deployment and operation of models. Technical Requirements: Experience with
Google Cloud Platform (GCP) , including GKE, AI Platform, Dataflow, and BigQuery services. Proficiency in
Python
and frameworks like
TensorFlow ,
PyTorch , or
Scikit-learn . Knowledge of
Kubernetes
and containerization (Docker). Experience with
CI/CD tools
such as Jenkins, CircleCI, or GitLab for ML pipelines. Strong knowledge of
DevOps
principles and tools (Terraform, Ansible). Preferred Qualifications: Hands-on experience with
MLFlow
or
Kubeflow . Familiarity with
data engineering
processes, ETL pipelines, and data lakes.