AI Engineer

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-Level 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
• 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