ML/AI Engineer

TechDigital Group; Princeton (NJ); On-site; USD 100,000 - 130,000; Full time

Job summary

A leading technology company in Princeton is seeking a Machine Learning Engineer to design and develop machine learning and deep learning models. This role involves integrating models into production systems, optimizing their performance, and collaborating with data scientists to achieve project objectives. Proficiency in Azure services and container management is essential. This position offers opportunities to work with cutting-edge technologies and scale impactful solutions.

Responsibilities

• Design and implement machine learning and deep learning models.
• Analyze large-scale datasets and integrate models into production.
• Optimize models for performance and scalability.

Skills

Machine learning

Deep learning

Data analysis

Python

Docker

Kubernetes

Azure Machine Learning

CI/CD pipelines

Model governance

LLM fine-tuning

Tools

Azure Kubernetes Service

Azure DevOps

MLflow

Terraform

Prometheus

Grafana

Job description

• Design, develop, and implement machine learning and deep learning models.
• Preprocess and analyze large-scale structured and unstructured datasets.
• Optimize models for performance, scalability, and efficiency.
• Integrate models into production systems using APIs or cloud-based deployment.
• Monitor, test, and retrain models as required based on feedback and performance.
• Document model development, architecture, and performance metrics.
• Design scalable infrastructure for training, deploying, and monitoring ML and LLM models in production.
• Manage Azure Kubernetes Service (AKS) clusters and containerized ML workloads.
• Ensure model governance, versioning, and reproducibility using tools like MLflow and Azure DevOps.
• Experience with Azure Machine Learning, Azure OpenAI, Azure DevOps, and AKS.
• Proficiency in Python, Docker, Kubernetes, and CI/CD pipelines.
• Experience with LLM fine-tuning, prompt engineering, and model deployment.
• Familiarity with MLflow, Terraform, and monitoring tools like Prometheus/Grafana.
• Collaborate with data scientists and domain experts to understand project objectives and define modeling approaches.