Head of Agentic AI Platform Engineering

Tiger Analytics

United States

On-site

USD 180,000 - 260,000

Full time

14 days+

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Job summary

Tiger Analytics seeks a highly experienced Lead AI Engineer to helm end-to-end AI engineering workstreams for the Luma platform. This hands-on technical leadership role drives architecture, design, and delivery of enterprise-grade Agentic AI solutions while serving as the primary technical interface for the client.

The ideal candidate blends deep AI engineering expertise with distributed systems, cloud-native architectures, and MLOps, mentors multiple pods, and champions engineering excellence

Qualifications

  • 10–15+ years in software engineering with 5+ years leading AI/ML engineering teams.
  • Experience building and deploying enterprise AI platforms on AWS.
  • Strong client-facing, solution architecture, and cross-functional leadership experience.

Responsibilities

  • Lead AI architecture and engineering roadmap for Agentic AI platforms.
  • Design and deliver scalable, production-grade AI solutions.
  • Oversee AI platform services, backend APIs, microservices, MLOps, observability, and cloud infrastructure.
  • Partner with clients to translate business needs into AI solutions and lead architecture discussions.
  • Mentor AI engineers, conduct design/code reviews, and drive engineering best practices.
  • Lead multiple engineering teams, manage delivery, risks, and technical roadmaps.
  • Evaluate and adopt emerging AI technologies and frameworks.

Skills

GenAI/LLMs
Distributed Systems
Cloud Architecture
Leadership
Client-facing
MLOps
Python

Tools

Python
Docker
Kubernetes
AWS
REST APIs

Job description

Tiger Analytics seeks a highly experienced Lead AI Engineer to helm end-to-end AI engineering workstreams for the Luma platform. This hands-on technical leadership role drives architecture, design, and delivery of enterprise-grade Agentic AI solutions while serving as the primary technical interface for the client.

The ideal candidate blends deep AI engineering expertise with distributed systems, cloud-native architectures, and MLOps, mentors multiple pods, and champions engineering excellence

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