AI Solutions Engineer

We are looking for an AI Solutions Engineer who not only builds cutting‑edge generative‑AI systems but also serves as the AI Enablement lead for the organization. You’ll design, develop, and productionize large‑language and multimodal model solutions while dedicating a significant portion of your time to help teams adopt AI responsibly and effectively.

• Collaborate with cross-functional teams to integrate and optimize generative AI solutions into products and processes.
• Develop domain specific AI software using RAG and Agentic AI.
• Collaborate with the data engineers, product teams, people operations, and legal to ensure compliant and responsible AI.
• Implement usage monitoring and bias mitigation to improve result quality.

• Act as the primary internal AI champion, providing guidance, training, and best‑practice workshops for the company.
• Create and maintain self‑service resources (playbooks, tutorials, templates) that empower users to build and evaluate AI solutions safely.
• Host regular “AI office hours,” one‑on‑one coaching sessions, and webinars to raise AI expertise across the company.
• Partner with compliance and ethics teams to embed responsible‑AI checks into everyday workflows.
• Gather user feedback, translate it into product requirements, and drive iterative improvements to AI tools and platforms.

• B.S. in Computer Science/AI/ML (or related) with 3+ years AI/ML engineering experience.
• Proficient in programming languages such as Python.
• Understanding of LLMs and multimodal models with their performance considerations and use cases.
• Knowledge about prompt engineering, RAG, and building AI Agents.
• Experience with ML/LLM libraries such as vLLM, LangChain, PyTorch, and HuggingFace.
• Practical experience developing, deploying, and scaling AI Agents in a production environment.
• Ability to translate complex technical concepts for non‑technical audiences and collaborate cross‑functionally.
• Experience running trainings to teach and promote AI best practices.
• Ability to create self‑service enablement assets (playbooks, tutorials, reusable component libraries).
• 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.

• Hybrid