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AI Tech Lead - US

StradITNew York, New York🇺🇸United StatesPosted 15 Jun 2026

Why This Role Stands Out

As an AI Tech Lead at StradIT, you'll spearhead the design and deployment of cutting-edge AI systems, driving innovation and technical excellence within a collaborative environment. This hybrid role offers significant growth potential and the chance to shape impactful AI solutions, perfect for experienced AI engineers who excel at leading teams and bridging research with production. Apply to leverage your expertise and advance your career in a dynamic setting!

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
New York, New York, United States
Posted
2 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.

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