Scan your resume against ATS criteria for this Senior AI Engineer role at 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.
We're hiring a 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.
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
How to apply for Senior AI Engineer at Tensorops?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
Salary details will be discussed during the interview.
What experience is required?
5+ years of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
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