Job Summary
The Principal AI Engineer will serve as the technical owner of how AI and machine learning engineering is designed, built, governed, and deployed across a portfolio of products. This is a hands-on engineering and technical leadership role spanning software engineering, data science, platform architecture, AI governance, security, and developer experience. The role will involve writing production code, architecting multi-tenant AI services, building reusable developer tooling, establishing security and governance practices, and mentoring engineering teams.

Key Responsibilities
• Design and own AI productization and governance playbooks covering service patterns, security and compliance standards, model evaluation rubrics, and production-readiness criteria.
• Build and maintain reusable internal tooling, including AI service scaffolding, evaluation and monitoring tools, and data infrastructure.
• Establish AI agent governance policies covering agent permissions, code execution controls, access to internal systems, and human-in-the-loop controls.
• Partner with Security, Legal, and Compliance teams to define SOC 2 and ISO-aligned AI controls, vendor DPA requirements, and prompt data classification policies.
• Establish standardized deployment patterns using containerization, infrastructure-as-code, and reusable CI/CD pipeline templates.
• Champion AI-assisted development practices, including LLM-integrated development workflows, test-driven development patterns, and reusable engineering tools.
• Establish and promote modern software development standards covering CI/CD, DevOps, testing, and delivery quality.
• Mentor engineers and technical leads to improve delivery consistency, design quality, and production-readiness practices.
• Serve as a technical authority on AI/ML, platform architecture, and engineering practices.
• Translate complex architectural decisions, AI risk considerations, and platform tradeoffs into clear guidance for technical and non-technical stakeholders.
• Lead the development and adoption of scalable AI and software engineering patterns across teams.

Required Qualifications
• 15+ years of experience 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 experience leading microservices architecture, including decomposing monoliths, defining service contracts, and operationalizing CI/CD for distributed systems.
• Strong hands-on experience with Docker, Terraform, and modern DevOps practices.
• Substantive experience with AI and LLM security, including prompt injection, data boundary enforcement, model supply chain risk, and AI-specific threat modeling.
• Strong problem-solving skills with experience building governance frameworks, evaluation rubrics, and reusable platform patterns at scale.
• Excellent written and verbal communication skills in English, with the ability to communicate complex technical concepts to diverse audiences, including executive stakeholders.
• Experience with Agile methodologies and cross-functional product team collaboration.

Preferred Qualifications
• Experience applying AI/ML in business consulting, advisory, or professional services environments.
• 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 SOC 2, ISO 27001, or TISAX frameworks.
• Experience developing internal technical guides, conducting workshops, or building developer education programs.
• Proficiency in Go or TypeScript.
• Demonstrated experience mentoring and developing engineers or technical peers.
• Willingness to work outside normal business hours when project requirements arise.
• Ability to work effectively in a hybrid office and remote environment.
• Willingness to travel based on client, team, and project requirements.

Certifications
• Advanced certifications in AI, deep learning, cloud architecture, or security, such as AWS, GCP, Azure ML, or CISSP, preferred.