Senior AI Platform Engineer – Productization & Governance

Midwestern

New York (NY)

Hybrid

USD 180,000 - 240,000

Full time

12 days ago
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Job summary

Midwestern is seeking a Principal Engineer to lead AI productization and platform governance across the firm’s internal tooling. You’ll own AI service patterns, security/compliance standards, and code-level architecture for scalable, secure AI capabilities used by senior partners and consultants.

You will mentor 5–7 engineers, drive cross-team collaboration, and shape how AI is built across the portfolio with a strong emphasis on reliability, threat modeling, and production-readiness in a hybrid

Qualifications

  • 12+ years in software engineering, data science, or a closely related technical field.
  • Experience designing and operating multi-tenant AI services in production.
  • Hands-on Python development with AI/ML frameworks.

Responsibilities

  • Design and own AI productization and governance playbook for internal platforms.
  • Build reusable internal tooling and data infrastructure for AI services.
  • Mentor engineers and tech leads to improve delivery quality and production readiness.
  • Partner with Security, Legal, and Compliance to define AI controls and data policies.
  • Establish deployment patterns using containers, IAC, and CI/CD templates.

Skills

AI/ML frameworks
Python
Multi-tenant AI services
CI/CD
DevOps
Team mentoring
Architectural leadership
Cloud deployment
Effective communication

Education

Bachelor's degree in Computer Science or Engineering

Tools

Docker
Terraform
CI/CD tooling

Job description

Midwestern is seeking a Principal Engineer to lead AI productization and platform governance across the firm’s internal tooling. You’ll own AI service patterns, security/compliance standards, and code-level architecture for scalable, secure AI capabilities used by senior partners and consultants.

You will mentor 5–7 engineers, drive cross-team collaboration, and shape how AI is built across the portfolio with a strong emphasis on reliability, threat modeling, and production-readiness in a hybrid

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