# AI Tech Lead

**Company:** [StradIT](http://jobs.workable.com/companies/gUMYmaPRJguCYquNxEjRoL.md)
**Location:** Jersey City, United States
**Workplace:** hybrid
**Employment type:** Full-time

[Apply for this job](http://jobs.workable.com/view/8326f547-223f-4c5f-8f5b-2d77bcb8897d)

## Description

We are seeking an experienced AI Engineering Lead to design, build, and scale advanced AI systems from model development through production deployment. The role involves leading the architecture of machine learning solutions, defining engineering best practices, and ensuring robust, reliable, and scalable AI workflows.

The ideal candidate has strong expertise in machine learning systems, data pipelines, and distributed computing, along with hands-on experience in Python and frameworks such as PyTorch, TensorFlow, or JAX. This role requires experience with cloud platforms (AWS, GCP, or Azure), containerization tools like Docker and Kubernetes, and familiarity with MLOps practices including model versioning, deployment, and monitoring.

You will collaborate closely with research, product, and engineering teams to deliver high-impact AI solutions, while also mentoring engineers and driving technical excellence across the organization. Strong leadership and communication skills are essential, along with a proven ability to bridge research and production environments.

This is a hybrid position for candidates located in NY/NJ that can commute to the office 4 days a week.

**Required Qualifications**

-   Lead the architecture and implementation of AI systems, from model development to deployment.
-   Define engineering best practices for AI workflows, model lifecycle, and system reliability.
-   8+ years of experience in software engineering, including 2+ years leading or mentoring teams.
-   Strong background in machine learning systems, data pipelines, or distributed computing.
-   Proficiency in Python (preferred), plus experience with frameworks such as PyTorch, TensorFlow, or JAX.
-   Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
-   Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.).
-   Strong understanding of model deployment, versioning, and monitoring.
-   Excellent communication skills and ability to collaborate with research and product stakeholders.