Mid-Level AI Engineer at tensorops.com

Job description

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 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

Requirements

Must-haves

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.
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)

Nice-to-haves

Experience working with stakeholders or clients is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlawsgcpazuremachine learningpythonci/cd

Platform: greenhouse
Effort: quick