AI Tech Lead - US

StradITJersey City, New Jersey🇺🇸United StatesPosted Jun 15, 2026

Why This Role Stands Out

As an AI Tech Lead at StradIT, you'll architect and scale cutting-edge AI systems, driving impactful solutions and defining best practices in a collaborative environment. This hybrid role is perfect for experienced AI engineers with a passion for leadership and innovation, offering excellent opportunities for growth and technical excellence. Apply today to shape the future of AI!

Quick Overview

Seniority

Mid Senior

Employment type

Full Time

Work mode

Hybrid

Location

Jersey City, New Jersey, United States

Posted

4 months ago

DockerGCPAWSMLOpsMLflowMachine LearningAirflowAzureKubernetesPyTorchPythonTensorFlow

Job 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.