Design, build, deploy, monitor, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and tooling.
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 roleWe'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
- 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
Similar Jobs
Cloud • Fintech • Food • Information Technology • Software • Hospitality
Lead a four-person engineering team building a new product from concept through MVP and beyond. Act as a player/coach by writing code, fixing bugs, triaging customer issues, shaping architecture, and driving rapid weekly sprints with Product. Establish lightweight engineering practices, coordinate cross-team dependencies, incorporate modern tools, and maintain close customer feedback loops while balancing speed, quality, and long-term viability.
Top Skills:
AILlms
.jpg)
Marketing Tech • Mobile • Software
Own Braze’s global AWS partnership strategy, Strategic Collaboration Agreement, roadmap, governance, executive alignment, co-sell motions, Marketplace revenue, and joint AI initiatives. Drive cross-functional execution across Sales, Product, Marketing, Engineering, and AWS teams while setting commercial targets and representing Braze in the technology ecosystem.
Top Skills:
Amazon RedshiftAmazon S3AWSAws AceAws BedrockAws Isv AccelerateAws MarketplaceAws Partner NetworkAws SagemakerAzureGoogle Cloud PlatformSaaS
Marketing Tech • Mobile • Software
Own Braze’s global AWS partnership strategy, including the Strategic Collaboration Agreement, governance, roadmap, executive alignment, co-sell motions, Marketplace revenue, and joint AI initiatives. Drive cross-functional execution across Sales, Product, Marketing, Engineering, and AWS stakeholders. Establish commercial targets, operationalize AWS partnership programs, influence integrations, and represent Braze across the AWS ecosystem. The role requires deep AWS expertise, strategic partnership leadership, enterprise GTM experience, and strong executive relationship management.
Top Skills:
Ai/MlAmazon BedrockAmazon RedshiftAmazon S3Amazon SagemakerAWSAws AceAws MarketplaceCppoEdpPpaSaaS
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
