Senior Machine Learning Engineer

Job Overview

Our client is seeking a highly skilled and experienced Senior Machine Learning Engineer to join their dynamic and innovative team. This role offers the opportunity to work remotely, providing flexibility and promoting a healthy work-life balance.

Responsibilities

Design and Develop: Create and optimize advanced machine learning solutions using Python and Spark, focusing on high-performance, parallel, and distributed computing methodologies.
Cloud Deployment: Utilize cloud platforms such as AWS, GCP, and Azure to deploy sophisticated machine learning models, demonstrating proficiency in data science platforms like SageMaker.
Pipeline Construction: Build and maintain robust machine learning pipelines using industry-standard tools like Airflow or Kubeflow, and meticulously track experiments with tools such as mlflow, TensorBoard, and SageMaker Experiments.
Model Scaling: Scale machine learning models to manage large datasets and high-dimensional feature spaces, maintaining a focus on model explainability and monitoring.
Distributed Computing: Use distributed computing frameworks like Apache Spark and GPU acceleration tools like CUDA for efficient model training and inference.
Model Optimization: Implement advanced model compression, quantization, and optimization techniques for deployment in resource-constrained environments.

Skills Required

Proficiency in Python, Spark, and data visualization libraries such as Matplotlib, Seaborn, and Plotly.
Extensive experience with cloud platforms (AWS, GCP, Azure), data science platforms (SageMaker), and CI/CD tools (Jenkins).
Familiarity with machine learning pipeline tools (Airflow, Kubeflow), experiment tracking tools (mlflow, TensorBoard), and API architectural styles (REST, Websocket, gRPC, SOAP).
Comprehensive knowledge of distributed computing frameworks (Apache Spark), GPU acceleration tools (CUDA), and version control systems (Git).
Understanding of model compression, quantization, optimization techniques, and secure model deployment tools (Docker, Kubernetes).
Familiarity with secure cloud environments (AWS, GCP, Azure), their security services, best practices, and Infrastructure as Code (IaC) tools (Terraform).

Educational and Experience Requirements

Bachelor’s Degree and a minimum of 9 years of directly relevant work experience.
Candidates with a PhD or Law degree and 6 years of experience, Masters degree and 7 years of experience, or Associates degree and 9 years of experience may also be considered.

About SSi People

With over 25 years of industry experience, SSi People has built its reputation and expertise on putting people first. Everything we do works toward delivering an exceptional experience for our consultants, our clients, and our internal team. Through a genuine commitment to people in everything we do, we have developed refined processes and a stellar internal team to deliver talent quickly. More importantly, we focus on building long-term relationships, not transactions. Putting people first is just what we do well.