Principal Software Engineer

Midwestern; New York (NY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Midwestern is seeking a Principal AI Engineer for its New York hybrid team. The role focuses on AI productization, platform engineering, and governance across a portfolio of internal products used by senior partners.

You will own secure AI design, multi-tenant services, and scalable tooling while mentoring 5–7 senior engineers and collaborating with product managers. The ideal candidate has 12+ years in software/data roles, deep AI/ML expertise, and hands-on cloud deployment, Docker, Terraform,

Qualifications

• 12+ years in software engineering, data science, or related technical field.
• Deep expertise in AI/ML frameworks and Python, with deployment to public cloud.
• Proven track record in multi-tenant AI services and LLM integrations in production.
• Experience leading microservices architectures and CI/CD for distributed systems.
• Hands-on with Docker, Terraform, and modern DevOps practices.
• Experience with AI/LLM security, prompt injection, and data boundary enforcement.
• Strong communication and ability to translate architectural decisions to executives.
• Experience with Agile and cross-functional product team collaboration.

Responsibilities

• Design and own AI productization and governance playbook, including service patterns and production readiness criteria.
• Build reusable internal tooling for AI services, evaluation, monitoring, and data infra.
• Establish AI agent governance policies for permissions and human-in-the-loop.
• Define SOC2/ISO-aligned AI controls and vendor data handling policies.
• Create standardized deployment patterns using containerization and IaC templates.
• Champion AI-assisted development practices and scalable tooling across teams.
• Mentor senior engineers and tech leads to improve delivery and quality.
• Bridge technical and non-technical stakeholders with clear guidance on platform tradeoffs.

Skills

AI/ML frameworks

Python ecosystem

LLM integrations

Microservices architecture

DevOps practices

Agile methodologies

Cross-functional collaboration

AI security

Prompt injection

Data boundary enforcement

Tools

Docker

Terraform

CI/CD tooling

Job description

About the Role - Principal AI Engineer - 12+ Years of Experience Required

Our client is a top-tier strategy and management consulting firm, the kind that competes with McKinsey, BCG and Bain for C-suite work. This role isn't on the client-facing side. It sits in their internal platform and product group, which builds and runs the 30+ products their consultants use to do that work. Your customers are the firm's senior partners, and they have little patience for AI features that sound good in a demo and fall apart in production.

The AI work falls into two buckets. Existing products are adding AI features and tooling. New products are being built from AI use cases that are still being defined, and you'll help define them. They're hiring Principal Engineers first, then building teams around them, so you'd get in early and shape how AI gets built across the portfolio.

They want engineers who have built, trained, and shipped real ML and LLM systems, not engineers who have mostly written prompts. Expect 70% to 80% of your time in the code, with the rest on architecture and tech leadership. You'll own how AI gets built securely, which means thinking about prompt injection and data boundaries before an incident forces the conversation. Infrastructure is handled by another team, so your focus stays on the AI itself. You'll mentor 5 to 7 senior engineers and work closely with product managers.

Location: New York (Hybrid - Midtown)

Work Arrangements: Open to 1099, C2C or W2

Duration: 12 month contract - high likelihood of extension based on performance

Travel: None

What You'll Do

AI Productization & Platform Engineering

• Design and own the firm's AI productization & governance playbook, covering service patterns, security/compliance standards, model evaluation rubrics, and production-readiness criteria.


• Build and maintain reusable internal tooling, including AI service scaffolding, evaluation and monitoring tooling, and data infrastructure - designed for team adoption without ongoing hand-holding.


• Establish AI agent governance policies covering agent permissions, code execution controls, access to internal systems, and human-in-the-loop enforcement.


• Partner with Security, Legal, and Compliance to define SOC2/ISO-aligned AI controls, vendor DPA requirements, and prompt data classification policies.


• Establish standardized deployment patterns using containerization, infrastructure-as-code, and CI/CD pipeline templates reusable across teams.

Developer Experience & Engineering Excellence

• Champion AI-assisted development practices across the engineering org, including LLM-integrated development workflows, test-driven development patterns, and reusable tooling standards that scale across teams.


• Codify modern software development standards (CI/CD, DevOps, testing, delivery quality) referenced by multiple teams as a baseline for new or re- platformed products.


• Mentor engineers and tech leads with observable improvement in delivery consistency, design quality, and production readiness rigor.

Cross-Functional Leadership & Stakeholder Influence

• Serve as the go-to technical authority on AI/ML, platform architecture, and engineering practices - regularly consulted by senior stakeholders at the design and strategy stages.


• Bridge technical and non-technical stakeholders, translating complex architectural decisions, AI risk topics, and platform tradeoffs into clear, actionable guidance.

What You'll Need

• 12+ years in software engineering, data science, or a closely related technical field.


• Deep expertise in AI/ML frameworks and the Python ecosystem, with hands-on experience deploying models to public cloud infrastructure.


• Demonstrated experience designing and operating multi-tenant AI services and LLM integrations in production.


• Proven track record leading microservices architecture - decomposing monoliths, defining service contracts, and operationalizing CI/CD for distributed systems.


• Strong hands-on command of containerization (Docker), infrastructure-as-code (Terraform), and modern DevOps practices.


• Substantive experience with AI/LLM security - including prompt injection, data boundary enforcement, model supply chain risk, and AI-specific threat modeling.


• Strong problem-solving skills, especially in building governance frameworks, evaluation rubrics, and reusable platform patterns at scale.


• Excellent written and verbal communication skills in English; ability to translate complex technical topics to diverse audiences, including executive stakeholders.


• Experience with Agile methodologies and cross-functional product team collaboration.