Build functional AI systems, not theoretical models Translate opportunity areas into working prototypes and scalable services Develop production-ready tools used by operators to create and optimize content Bridge innovation, engineering, and production execution Partner with research and analytics teams to translate insights into deployable AI logic Responsibilities AI System Design & Engineering Design and develop end-to-end AI-powered services for content production workflows Build modular components (APIs, services) that integrate with enterprise platforms Leverage LL - Ms, RAG pipelines, diffusion models, and vector databases Ensure systems are extensible, reusable, and production-safe Build modular, extensible systems using Infrastructure as Code (e.g., Terraform) to enable repeatable environments, scalable architecture, and seamless service integration. Po. C to MVP to Production Delivery Rapidly prototype capabilities to validate feasibility Evolve prototypes into MVP tools with real operator usage Productionize systems with scalability, reliability, and workflow integration Workflow & Tooling Innovation Build AI-assisted generation pipelines and content optimization tools Develop reusable, modular capabilities that scale across clients Enable self-service and assisted creation models for operators Intelligence Layer Development Engineer systems that convert data and performance signals into actionable inputs (e.g. integrated content recommendation engine) Guide content creation, adaptation, and optimization decisions Integration with Production Ecosystem Integrate solutions into production environments including workflow and DAM systems Ensure outputs are telemetry-driven, traceable, governed, and compliant across systems and workflows. Engineering Standards & Governance Design and implement CI/ CD pipelines (e.g., Git-based workflows) to support automated build, test, and deployment for reliable, continuous delivery. Build systems that adhere to secure data handling and enterprise security standards. Ensure observability, monitoring, and auditability of systems Qualifications 7–10 years in software engineering, AI/ ML engineering, or related fields 2–5 years' experience in content production-grade software Strong Python proficiency and experience with modern AI/ ML frameworks Experience building and deploying production-grade systems Familiarity with LL - Ms, RAG, generative models, and cloud-native architectures Experience with APIs, microservices, and system integration Ability to translate complex requirements into scalable engineering solutions Strong collaboration skills across technical and non-technical teams What Success Looks Like Po. Cs consistently evolve into production tools used by operators AI-powered systems are embedded into content production workflows Capabilities are delivered as reusable, scalable services The organization operates as a product-building engine, not just strategy AI becomes infrastructure within production, not experimentation Technical Competencies Python, APIs, and modular system architecture (microservices) LL - Ms, advanced prompt engineering system design, and RAG pipelines Vector databases (e.g., Pinecone, FAISS) and data modeling (SQL) Generative AI systems (diffusion models, Comfy. UI) and fine-tuning methods (Lo. RA) Cloud platforms (GCP/ AWS), Docker, Kubernetes, and Terraform (Ia. C) CI/ CD pipelines (Git-based workflows, automated build/test/deploy) Workflow orchestration and enterprise integrations (DAM, CMS, Workfront) Media processing pipelines (image/video/3 D asset handling – e.g. GLB, OBJ, STL, CAD, RAW) Telemetry, logging, monitoring, and model evaluation/governance