About The Role

Join Divergent, a technology company revolutionizing the manufacturing industry with its innovative Divergent Adaptive Production System (DAPS). As an AI Solutions Engineer, you will design, develop, and implement cutting-edge AI solutions while promoting responsible AI adoption across the organization. You will collaborate with cross-functional teams, create self-service resources, and drive iterative improvements to AI tools and platforms. This is an exciting opportunity to shape the future of technology and production.

Concevoir, développer et mettre en production des solutions basées sur des modèles de langage et multimodaux.
Collaborer avec des équipes interfonctionnelles pour intégrer et optimiser les solutions d'IA générative dans les produits et processus.
Agir en tant que champion interne de l'IA, en fournissant des conseils, des formations et des ateliers sur les meilleures pratiques.
Experience running trainings to teach and promote AI best practices
Ability to create self‑service enablement assets (playbooks, tutorials, reusable component libraries)
Knowledge about prompt engineering, RAG, and building AI Agents
Experience with ML/LLM libraries such as vLLM, LangChain, PyTorch, and HuggingFace
Ability to translate complex technical concepts for non‑technical audiences and collaborate cross‑functionally
Understanding of LLMs and multimodal models with their performance considerations and use cases
Practical experience developing, deploying, and scaling AI Agents in a production environment
Proficient in programming languages such as Python
B.S. in Computer Science/AI/ML (or related) with 3+ years AI/ML engineering experience
Proven track record of gathering user feedback and iterating on AI tools to improve adoption
Ability to lawfully access information and technology that is subject to US export controls
M.S. in Computer Science, AI, ML, or related field
Proficiency in TypeScript
Knowledge of ethical AI, compliance frameworks, and safety standards
Experience with multimodal pipelines and traditional AI/ML methods
Hands‑on MLOps: CI/CD, Docker, Kubernetes

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