ML Architect

TechDigital Group; Princeton (NJ); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading technology firm is seeking an experienced ML Architect in Princeton, NJ to design scalable infrastructure and implement end-to-end MLOps pipelines using Azure technologies. The ideal candidate will work closely with data scientists and engineers, ensuring model governance and high availability of production ML systems. This role requires strong expertise in Azure, Python, and DevSecOps practices.

Qualifications

• Experience in architecting and implementing end-to-end MLOps and LLMOps pipelines.
• Strong knowledge of Azure Kubernetes Service (AKS) and containerized workloads.
• Proven ability to monitor and troubleshoot production ML systems.

Responsibilities

• Document architecture, workflows, and best practices for knowledge sharing.
• Provide technical oversight and guidelines.
• Design scalable infrastructure for ML and LLM models in production.
• Collaborate with data scientists to streamline workflows.
• Manage AKS clusters and containerized ML workloads.

Skills

Experience with Azure Machine Learning

Experience with Azure OpenAI

Proficiency in Python

Docker

Kubernetes

CI/CD pipelines

LLM fine-tuning

Prompt engineering

Tools

MLflow

Terraform

Monitoring tools (Prometheus/Grafana)

Job description

ML Architect

Responsibilities

• Document architecture, workflows, and best practices for knowledge sharing and compliance.
• Provide technical oversight & Guidelines
• Architect and implement end-to-end MLOps and LLMOps pipelines using Azure Machine Learning and Azure OpenAI.
• Design scalable infrastructure for training, deploying, and monitoring ML and LLM models in production.
• Collaborate with data scientists and engineers to streamline model development, testing, and deployment workflows.
• Manage Azure Kubernetes Service (AKS) clusters and containerized ML workloads.
• Ensure model governance, versioning, and reproducibility using tools like MLflow and Azure DevOps.
• Promote DevSecOps practices, ensuring security and compliance are embedded in the ML lifecycle.
• Monitor and troubleshoot production ML systems, ensuring high availability and performance.

Qualifications

• Key Skills - 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.