C
Principal AI Engineer
Compunnel
2 hours ago
Full-time
On-site
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.
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.