Principal AI Engineer

Phizenix•New York, NY

14h•Hybrid

About The Position

The Principal AI Engineer sits at the intersection of software engineering, data science, platform architecture, and AI governance. You will be the technical owner of how AI/ML engineering is designed, built, governed, and shipped across a portfolio of products serving all of <our consulting company> domains. This is a hands-on engineering and technical leadership position. You will move fluidly between writing production code, architecting multi-tenant AI services, building internal developer tooling, leading security and governance design, and mentoring engineering teams. Your success is measured by team-level outcomes — scalable patterns that outlast your direct involvement.

Requirements

15+ years in software engineering, data science, or a closely related technical field.
Bachelor's degree or higher in Computer Science, Engineering, or a related 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.

Nice To Haves

Experience applying AI/ML in business consulting, advisory, or professional services contexts.
Familiarity with AI service interface and gateway design patterns, including emerging AI integration protocols.
Contributions to open-source AI/ML projects, publications, or active involvement in technical communities.
Advanced certifications in AI, deep learning, cloud architecture, or security (e.g., AWS/GCP/Azure ML, CISSP).
Experience defining AI compliance controls for SOC2, ISO 27001, or TISAX frameworks.
Demonstrated enthusiasm for developer education — writing internal guides, running workshops, or building internal tooling communities.
Proficiency in additional languages is a plus (Go, Typescript)
Demonstrated ability and enthusiasm to mentor and uplift junior team members or peers
Willingness to work outside of normal business hours, and in particular as unique projects/needs arise.
Ability to work full time in an office and remote environment; physically able to sit/stand at a computer and work in front of a computer screen for significant portions of the workday
Must become familiar with, and promote and abide by, our Core Values as defined by the <our consulting company> and foster an inclusive environment with people at all levels of an organization

Responsibilities

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.
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.
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.