## ## About TensorOps TensorOps is a boutique AI consultancy that bridges strategy and execution, we design and ship production-grade AI systems for enterprise clients, from Fortune 500 companies to fast-growing unicorns. Our work spans agentic AI, LLM fine-tuning, RAG systems, and ML-driven products, deployed on AWS, GCP, and Azure. We've shipped AI systems impacting 200M+ end users daily, partnered with 11 unicorns and NASDAQ-listed companies (including Notion, ServiceNow, JFrog, Seeking Alpha, Armis, and GoCardless), and get 95% of validated ideas into production within two months. We're Google Cloud, AWS, and Cloudflare partners, and we're 100% remote by design. ## About the role We're hiring a Mid/Senior ML Engineer to contribute to technical direction across client engagements and mentor a growing team of junior ML engineers. You'll work directly with clients, taking AI systems from prototype to production-grade deployment. In this role, you will: - Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients - Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration - Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions - Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing - Help shape internal best practices, tooling, and technical standards as the team grows - Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences You’ll be part of a supportive, fast-growing team that values autonomy, open communication, and continuous learning. ## Requirements - 2+ years of professional experience in Machine Learning, AI Engineering, or a related role (Mid-level) / 5+ years for Senior - Strong hands-on skills in Python, writing clean, efficient, well-documented, production-quality code - Proven experience designing, training, optimizing, and deploying ML models independently (e.g., PyTorch, TensorFlow, Scikit-learn) - Experience building GenAI & LLM systems: RAG pipelines, chatbot architectures, and applications using tools like LangChain - Familiarity with MLOps & production ML practices: model versioning, monitoring, CI/CD for ML workflows - Experience deploying and scaling ML systems on AWS, GCP, or Azure - Strong performance optimization and debugging skills (diagnosing complex issues and improving system reliability and efficiency) - Experience working with stakeholders or clients is a plus ## What We Offer - 100% Remote Work: no mandatory office days, work from wherever - Funded certifications: fully paid AWS and GCP professional certifications - Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries - International Clients: Collaborate with global organizations and solve real-world challenges at scale - Urban Sports Club Membership: Supporting your physical and mental wellbeing - Monthly Bolt Credits: For rides - Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate