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About the Role
The Principal AI Engineer is a hands-on technical leadership position responsible for the design, development, governance, and deployment of AI/ML engineering across a portfolio of products. This role involves writing production code, architecting AI services, building developer tooling, leading security and governance, and mentoring teams, with success measured by team-level outcomes and scalable patterns.
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.
- 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.
- 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.
Requirements
- 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.
- 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.
- 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.
- 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.
Skills
- AI/ML frameworks
- Python ecosystem
- Deploying models to public cloud infrastructure
- Multi-tenant AI services
- LLM integrations
- Microservices architecture
- Containerization (Docker)
- Infrastructure-as-code (Terraform)
- DevOps practices
- AI/LLM security
- Prompt injection
- Data boundary enforcement
- Model supply chain risk
- AI-specific threat modeling
- Governance frameworks
- Evaluation rubrics
- Reusable platform patterns
- Agile methodologies
- Cross-functional product team collaboration
- AI service interface design
- AI gateway design patterns
- Go
- Typescript
Location
- New York (Hybrid - Midtown)
Work Type
- Hybrid
- 1099
- C2C
- W2
- Contract
Experience Level
- Principal
- 12+ years
Education Level
- Bachelor's degree or higher in Computer Science, Engineering, or a related field preferred.
- Advanced certifications in AI, deep learning, cloud architecture, or security (e.g., AWS/GCP/Azure ML, CISSP).