Company Name: TensorOps

Job Details: Hiring,Remotely,in,USA,Remote,Mid,level

Job Url: https://builtin.com/job/mid-level-ai-ml-engineer/7366525

Job Description: Build the Next Generation of AI Products with TensorOpsTensorOps is an applied-machine-learning studio that helps organisations across Europe and North America design, train, and deploy production-grade GenAI systems. Our team blends research depth with pragmatic engineering, and we’re looking for experienced engineers to help us build and scale our solutions.What We’re Working OnConversational copilots that assist knowledge workersAutonomous research agents for market-leading platformsDecision-support tools for healthcare, finance, and e-commerceCore StackPython, FastAPI, DockerTensorFlow, LightGBM, CatBoostOpen-source & commercial LLMsLangChain / LangGraph, Langfuse, MCPMLFlow, KubeflowAWS and GCPThe RoleAs a Mid-Level Machine Learning Engineer, you will be a key contributor to our project teams, taking ownership of core components and shipping robust AI/ML systems.You will:Design, build, and maintain production-grade ML systems, from data ingestion and processing to model deployment and monitoring.Develop and fine-tune generative AI models, including LLMs, for specialized tasks. You'll move beyond prototyping to build robust, scalable solutions.Architect and implement reliable data pipelines and low-latency inference services using our core stack (FastAPI, Docker, Kubeflow, AWS/GCP).Collaborate with senior engineers, researchers, and client stakeholders to translate business problems into technical solutions and deliver tangible value.Take ownership of key components of our ML platform, ensuring code quality, performance, and scalability.About You3+ years of professional experience in a software engineering or machine learning role.Strong proficiency in Python and its data science ecosystem (e.g., Pandas, NumPy, Scikit-learn).Hands-on experience building and shipping models using at least one major ML framework.Proven experience with the practical application of Large Language Models (LLMs). Familiarity with frameworks like LangChain/LangGraph and retrieval-augmented generation (RAG) is a significant plus.Solid understanding of software engineering best practices, including version control (Git), testing, CI/CD, and containerization (Docker).A BSc/MS in Computer Science, Software Engineering, or a related field, or equivalent practical experience.Why TensorOpsHigh-Impact Projects: Work on challenging, real-world problems for industry-leading clients, seeing your work move from concept to production.Expert Collaboration: Join a team of experienced ML engineers and researchers. We foster a culture of deep collaboration and knowledge sharing.Career Growth & Ownership: We offer competitive compensation and provide clear paths for career progression. Take ownership of critical systems and grow into a senior role.