Senior AI Engineer I

As an AI Engineer at Loop, you'll play a critical role in transforming how our teams work by developing AI-driven tools that solve business challenges. Collaborating closely with internal stakeholders, you'll turn complex needs into simple, scalable solutions. Whether it's automating customer feedback analysis, streamlining campaign workflows, or building tools that improve asset creation, your work will directly influence how teams operate. By blending technical expertise with a deep understanding of our business, you'll empower every department to achieve more-and faster. This is your chance to create tools that drive impact across the company., We're looking for an AI Engineer who takes full ownership of building impactful AI solutions-from scoping to deployment. You'll design, develop, and ship production-ready systems that automate workflows, accelerate decision-making, and save hours across teams. Working closely with stakeholders, you'll translate real business challenges into intelligent, scalable tools using NLP, LLMs, and agentic workflows. You'll choose the right stack-Streamlit, FastAPI, Azure, or GCP-and ensure adoption through seamless integration and measurable outcomes. This is a hands-on, high-autonomy role where you'll help define both the technical direction and engineering standards., * Develop AI-driven software to solve business challenges, integrating technologies like NLP, machine learning, and automation.

• Build and deploy applications using open-source frameworks (Streamlit, Gradio, or FastAPI), so they can be used by other teams.
• Create custom integrations using APIs and webhooks to connect AI solutions with internal systems.
• Fine-tune LLMs for specific business applications.
• Own projects and products end-to-end, from identifying opportunities to deployment, using first-principle thinking.
• Stay ahead of AI trends and recommend new technologies to enhance our capabilities., Success in this role means combining technical excellence with creativity, autonomy, and leadership. You'll take full ownership of projects-from defining scope and technical direction to driving measurable business outcomes like adoption, automation impact, and efficiency gains. You'll set engineering standards, mentor teammates, and foster a culture of experimentation and excellence. Clear communication and collaboration with stakeholders will be essential as you translate complex challenges into simple, scalable solutions. By staying ahead of AI trends and sharing your knowledge, you'll elevate the team and shape how Loop builds with AI., * Within your first month, you'll immerse yourself in Loop's workflows and AI ecosystem-learning how different teams operate and identifying opportunities where AI can make the biggest impact. You'll also help establish best practices for development, deployment, and collaboration, setting the foundation for scalable AI delivery.
• By your third month, you'll take ownership of an internal AI product, working closely with stakeholders to scope requirements, design system architectures, and build the first working prototypes.
• By your sixth month, you'll be leading multiple AI products from a technical perspective -owning one from concept to production deployment. You'll have contributed to Loop's growing library of internal AI tools, mentoring teammates and helping define the standards for how we build, deploy, and scale AI solutions across the organization.

Do you have experience in Python?, * 5+ years of experience building AI/ML algorithms and solutions, and a degree in Computer Science or related field.

• Highly proficient with Python, and with ML frameworks like PyTorch and scikit-learn.
• Hands-on experience with Natural Language Processing (NLP), traditional ML, and LLMs.
• Experience with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects.
• Familiarity with the latest in GenAI, including RAG, agentic workflows, MCP, A2A, tool use, function calling, and multi-modal systems.
• Experience with DevOps practices, including CI/CD pipeline tools such as Docker, Kubernetes, Github Actions.
• Experience with deployment on one of the major cloud providers (preferably Azure/GCP).
• Understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks.
• Experience with MLOps, including monitoring and optimizing AI models for performance, scalability, and efficiency.