Principal AI Engineer
Hands-on technical leader who owns how AI/ML is designed, built, governed, and shipped across the consulting firm's portfolio — writing production code, architecting multi-tenant AI services and LLM integrations, building internal developer tooling, and leading AI security/governance design with Python, Docker, and Terraform.
- Compensation
- Not disclosed
- Location
- Midtown Manhattan, New York City
- Work setup
- Hybrid
- Seniority
- Principal
- Employment
- Full time
- Experience
- 15+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 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
- Experience designing and operating multi-tenant AI services and LLM integrations in production
- Proven track record leading microservices architecture — decomposing monoliths, defining service contracts, operationalizing CI/CD
- Strong hands-on command of containerization (Docker), infrastructure-as-code (Terraform), and modern DevOps practices
- Substantive experience with AI/LLM security — prompt injection, data boundary enforcement, model supply chain risk, AI-specific threat modeling
- Strong problem-solving skills in building governance frameworks, evaluation rubrics, and reusable platform patterns at scale
- Excellent written and verbal communication skills in English; able to translate complex technical topics to executive stakeholders
- Experience with Agile methodologies and cross-functional product team collaboration
Nice to have
- 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 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
- Enthusiasm for developer education — internal guides, workshops, internal tooling communities
- Proficiency in additional languages (Go, TypeScript)
- Demonstrated ability and enthusiasm to mentor and uplift junior team members or peers
- Willingness to work outside of normal business hours as unique projects/needs arise
- Ability to work full time in an office and remote environment
Full description
Job description
At <our consulting company>, we solve the most complex and critical challenges by moving quickly from analysis to action when it really matters; creating value that has a lasting impact on companies, their people, and the communities they serve. We hold ourselves accountable by providing space for authenticity, growth, and equity for everyone.
<Our consulting company> has embraced a hybrid work model to provide flexibility and support work-life integration. Travel is part of this position, but frequency may vary based on client, team, and individual circumstances. Relocation assistance is not available for this position.
About the Role
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.
What You'll DoAI 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.